$212,260estimated top of the range · middle $130,000 / yr
AI is transforming this role
Data Engineers in the United States earn a median of $130,000 a year. Pay starts near $82,000. The top of the range is estimated at $212,260. The Bureau of Labor Statistics does not publish a separate wage series for this exact title, so this figure is derived from the closest occupation it does track and is labelled an estimate.
Source: PayCrunch estimate. Last checked 9 September 2026.
Entry level
$82,000
Top-end estimate
$212,260
Education
Bachelor's degree in Computer Science
Wages — PayCrunch estimate. The Bureau of Labor Statistics does not publish a separate wage series for Data Engineer; figures are derived from the closest occupation it does track and are labelled as estimates. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for Data EngineerReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Data Engineer work right now.
Claude CodeNEWFree / usage-based
Terminal coding agent that reads your repo, runs tests, and ships multi-file changes.
How a Data Engineer uses it: describe a feature and let it implement and test it across the codebase
OpenAI CodexNEWIncl. w/ ChatGPT plans
Agent that runs longer, deterministic multi-step coding jobs on its own.
How a Data Engineer uses it: delegate a well-defined build or migration and review the finished result
WindsurfNEWFree / $15 mo
Agentic IDE that keeps context across a whole project.
How a Data Engineer uses it: make large, coordinated changes without losing track of the codebase
AWS KiroNEWPreview / see site
Spec-driven coding agent that turns written specs into working code.
How a Data Engineer uses it: write the spec first and let it build to that spec
NotebookLMNEWFree / $7.99 mo
Google tool that answers questions grounded only in the documents you give it — with citations.
How a Data Engineer uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source
CursorFree / $20 mo
AI-native code editor that edits across an entire project.
How a Data Engineer uses it: describe a change in plain English and let it rewrite and refactor whole files
GitHub Copilot (Agent Mode)$10–19 mo
AI pair-programmer built into VS Code and GitHub that now completes multi-step tasks.
How a Data Engineer uses it: hand off a task and have it plan, edit multiple files, and open a pull request
ChatGPTFree / $20 mo
The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.
How a Data Engineer uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions
ClaudeFree / $20 mo
AI assistant known for careful writing, long-document analysis, and coding.
How a Data Engineer uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
A failure notice is already in the queue when the data engineer opens the laptop, and the warehouse table finance needs for the morning close is empty. Somewhere between the billing database and that table, a job stopped. The engineer finds the broken step, repairs the load, and checks that the rows which did arrive are fit for other people to use. Analysts will chart the result later. An architect may already have decided where this subject should live. This morning the work is the pipeline itself.
That rescue is only the visible part of the occupation. Most days are spent building the movement and the cleaning that keep tables trustworthy when nobody is watching: schedules, transformations, tests, and a way for a colleague to know the data is ready. Anyone aiming at the title should learn to talk about a pipeline another team depends on. The hire and the salary conversation both turn on that dependence.
Pipelines other people can trust
A pipeline takes data from a source, changes it into a shape consumers expect, and lands it where they can query it. Sources include application databases, event streams, vendor files, and spreadsheets a department refuses to give up. The engineer agrees the extract with the source owner, handles late and duplicate arrivals, and writes transformations that rename fields, resolve keys, and drop test traffic. The landing zone may be a cloud warehouse, a lake of files, or a serving table an application reads. The point is usability. If an analyst still has to repair the same mess every Monday, the pipeline is unfinished.
Cleaning inside the pipeline is different from a one-off analysis. Rules live in code that runs every cycle: a key must be present, a status must be one of the agreed values, a total must reconcile to the source within a tolerance the business has accepted. When a check fails, the job should stop or mark the table stale rather than quietly publish a wrong number. Orchestration ties the steps together so a downstream model does not start on an upstream failure. Backfills, the reprocessing of history after a rule changes, are part of the design, not an emergency invented at midnight.
The tool shelf depends on the company. SQL does a large share of the transformation work. Python shows up for awkward files and for services. Orchestrators such as Airflow, and transformation frameworks such as dbt, are common enough to meet in job postings. Warehouses from the major cloud vendors appear constantly. Streams and processing engines appear where the business needs fresher data. Learn the ideas underneath the brands: idempotent loads, clear dependencies, secrets kept out of code, and a run history someone else can read. A new brand can be picked up. A habit of unpublished one-off scripts cannot.
The neighbors define the boundaries of the role. The analyst takes a trustworthy table and answers a business question with a chart or a table of their own. The architect decides how the subject ought to be stored and which systems may share it. The engineer builds and runs the pipelines that move and clean the data so both of those colleagues, and the operators beyond them, can do their work. On a healthy team the three talk often. The engineer who ignores the architect’s model creates a private path. The engineer who ignores the analyst ships a table nobody can explain. The useful engineer sits in both conversations and still owns the job that has to succeed before breakfast.
Showing the work without a licence
No licence board admits people to data engineering. Employers hire on evidence that you have moved real data and kept it fit for use. A computer science or information systems degree is a common start. Software engineers cross over. Analysts cross over after they tire of repairing the same extract by hand and start encoding the repair. A certificate from a cloud vendor can show you have studied a platform. It will not carry the interview if you cannot describe a pipeline, its failures, and its consumers.
A public portfolio helps when your production code is private. Build a small project that ingests a published dataset, transforms it with tested steps, and documents how a reader would know the load succeeded. Put the code where a reviewer can run it. Write a page about one failure you forced on purpose and how the check caught it. Strip any employer data. What remains is judgment: you know the difference between a demo script and a job someone could schedule. Inside a company, the same proof is operational. Name the pipeline, the source, the consumers, and the incident you shortened by making a failure obvious.
Keep a log of operability, because that is what senior people ask about. How do you deploy a change? How do you roll it back? Who gets told when a table is late? Where do credentials live? Candidates who answer with a story from a real system pass the part of the interview that trivia cannot fake. If your current title is analyst or software developer, propose one pipeline at work and see it into production with a partner who will use it. That single shipped path is a stronger application than a stack of unfinished tutorials.
Three seats, three products
The analyst’s product is a chart or a table that answers a business question. The architect’s product is a decision about storage and sharing. The engineer’s product is the pipeline that moves and cleans data until those other products can be built on something solid.
Getting onto a platform team
Search for data engineer, analytics engineer, and ETL developer. Platform engineer can mean this work or a more general infrastructure role, so read the duties. You want postings that mention ingestion, transformation, orchestration, data quality, and a warehouse or lake other teams query. A posting that is only dashboard building is a different job. A posting that is only a high-level storage strategy, with no pipelines to run, is closer to architecture. The engineer posting admits that jobs fail and someone must own the repair.
Write the resume as a series of pipelines in production. For each, give the source, the destination, who consumed it, and one hard part: a late file, a changing schema, a backfill, a check that stopped a bad publish. Mention the on-call expectation if you have lived it, and say what you changed so the same page stopped repeating. Tool lists belong after those stories. A hiring manager scanning for “built” and “maintained” will not be persuaded by a brand parade with no owner.
Interviews mix coding with operations. You might transform a sample in SQL, talk through how you would ingest a new source, or explain how you would reprocess a month after a bug. Describe tests, alerting, and the conversation with the consumer who was waiting. Some teams offer a short exercise. Keep it boring and correct: clear steps, a check, a note about what you would harden if it ran nightly. Ask how the team splits work with analysts and with any architect on staff. Ask who carries the pager, and whether source teams are required to announce breaking changes. Those answers predict your first six months better than a slogan about modern stacks.
Contract roles and internal transfers are both common doors. A software engineer who already understands services can learn the warehouse’s habits. An analyst who has been writing the transformation in a notebook can ask to move that logic into the scheduled layer with a mentor. Either way, ship something small that another person uses, then apply. Referrals help, and they help most when the referrer can say you left the pipeline better than you found it, not merely that you are eager.
From one job to the platform
The first assignment is usually one domain’s pipelines under a senior engineer: orders, events, or a finance feed. Learn the source team’s calendar, the consumers’ deadlines, and the failure modes that are actually likely. A senior data engineer then designs patterns others copy: how a new source should be onboarded, how checks are written, how backfills are requested. Staff engineers own the paved road, the shared platform pieces that make a new pipeline dull in the best sense. Dull and reliable is the compliment.
Management is a fork, not a requirement. An engineering manager hires, sets the on-call load, and negotiates with source teams who want exceptions. A principal engineer stays on the hardest platform problems and reviews designs. Some people later move toward architecture, carrying a builder’s knowledge into storage decisions. Some move toward analytics engineering embedded with a business unit. If you want to remain the person who makes data usable, keep at least one pipeline you still understand end to end. Leaders who cannot read a failing job become dependent on heroics they can no longer coach.
Growth also means better partnerships. Spend time with the analyst who found a wrong total, and change the pipeline so that class of error cannot hide. Spend time with the architect when a new product wants a side door into core data, and implement the shared path instead. Write short runbooks. Teach a junior how you decide a job is safe to rerun. The career compounds through systems that survive your vacation. When you can take a week away and the close still happens, you have left the first job behind.
Placing an offer on the estimate
Put the estimate label down before the dollars. A separate Bureau of Labor Statistics wage series for this exact title has not been published, so the entry, the middle, and the high end a data engineer can cite are estimates, and none of them is pinned to a state. Entry is $82,000. The median is $130,000. The estimated high end is $212,260. Entry and the median differ by $48,000. The gap from the median to the estimated high end is $82,260.
Read $82,000 as the estimate for a starting seat: a junior engineer paired with a senior, one pipeline, and reviews on every change. If you already operate jobs that other teams schedule their morning around, an offer stuck at that entry estimate is worth a direct comparison with the median. The $48,000 between $82,000 and $130,000 is the step up to independent ownership. Describe the pipelines, the consumers, and the checks you put in place. Call $130,000 the median estimate, not a published wage for the title and not a city rate.
The estimated high end of $212,260, and the $82,260 above the median, belong to scarce scope: staff ownership of a platform, a lead role across many domains, or a market where the employer has struggled to hire. Raise that figure when the interview was about platform strategy and on-call design for a large group. Keep it as context, still labeled an estimate, when you are changing jobs at the middle of the range. Do not treat it as the opening number for a first production pipeline. Because these are estimates rather than state medians, they will not price a move from one city to another. Ask the employer for the band on this requisition and see which of the three estimates that band resembles.
Convert the offer to an annual base you can verify. Bonus and equity can be real money, and only the letter can say how much. Compare the base, plus cash the letter guarantees, with $82,000, $130,000, and $212,260. If a recruiter quotes a single figure with no scope, ask whether you will own pipelines or only assist, then pick the estimate that matches the answer. A median-shaped workload paid at the entry estimate is the mismatch to name, using the $48,000 gap as the size of the mismatch and nothing beyond what this page’s estimates support.
When the pipelines you describe are ones other people already trust, set the company’s number against $82,000, $130,000, and $212,260, and keep the word estimate attached to each.
The top of Data Engineer pay — and how to get there with AI
$212,260top-end estimate for Data Engineer
PayCrunch estimate - derived from the closest occupation BLS tracks (Database Architects, 15-1243). This figure is PayCrunch’s estimate, not a Bureau of Labor Statistics published wage for this exact title.
And the role it leads to — Computer and Information Systems Managers — reaches $327,300 in Washington.
$82,000entry$130,000middle$212,260top end
Two engineers keep identical pipelines alive; the one paid at the top of this range has handed the debugging, the deployment and the runbook to people who used to page them, and spent the recovered hours on work nobody else could do.
This role fills up with troubleshooting support for data warehouses, and firefighting is the single activity that leaves nothing behind when it ends. Testing systems for enhancements, reviewing test plans, verifying that warehouse data is accurate, writing programs in whatever language the shop uses, all of it can be taught. Someone who teaches it stops being the single point of failure a company both depends on and declines to promote. Code assistants have made pipeline code cheap to produce and expensive to trust, so being able to teach a colleague how to read a produced transformation sceptically is now rarer than being able to write one quickly.
Your playbook, by where you are now
Just startingWrite down whatever you had to ask
Keep a note of every question you needed a colleague to answer, and turn each into a paragraph anybody could read.
Adopt the pipeline nobody wants and learn its failure modes properly, since that is the one you will eventually hand over.
Define one job's deployment in Ansible software so restoring it does not depend on anyone remembering.
Sit beside an analyst through their first Amazon Redshift load instead of doing it for them.
Ask Claude to explain an unfamiliar Apache Cassandra access pattern, then confirm the behaviour on a throwaway table before relying on it.
What proves it: A runbook a colleague used to clear an incident without calling you.
Realistic span: years one and two
A few years inHand over the pager on purpose
Set up a rotation where somebody else takes first response on your pipelines while you sit behind them.
Teach the transformation tooling by getting two analysts to ship a change through review, not by demonstrating it on your screen.
Publish the checks that verify structure and accuracy of warehouse data and walk a new starter through adding one.
Hold a monthly session on a failure of your own: what broke, what the log looked like, what you changed afterwards.
Convert recurring requests into self-serve jobs so the queue shrinks rather than moving faster.
What proves it: A pipeline you built that other people now operate and extend without you.
Realistic span: the middle years, roughly three through seven
ExperiencedOwn how the company builds
Design the platform training every engineer joining the data function goes through, and keep teaching part of it yourself.
Set the review rules for produced code, naming exactly what has to be checked before a merge.
Take the projects nobody has attempted, a new source system mapped end to end or a migration off something ancient, because your old work now runs without you.
Aim at technical management if you want the budget, and note California pays this occupation best.
What proves it: A training path and review standard the whole data function runs on.
Realistic span: eight years in and onward
The next 90 days
Choose the pipeline you are most often called about and spend ninety days getting somebody else able to fix it. Start by writing the runbook from the last ten incidents, one page per symptom, with the actual query or command in it rather than a description. Then pick a colleague, tell them they take the next failure, and sit quietly behind them while they work through your page. Everything they get stuck on is a gap in what you wrote, so fix it that afternoon. Repeat until a failure is resolved without you being asked. A data engineer who has done this once has an argument for being given harder work, and a reason to expect it: the last thing they owned did not fall over when they stopped watching it.
Wage figures: PayCrunch estimate. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.
Never used AI before? Start here (2 minutes).
Turn on an AI assistant inside your warehouse and IDE this week. Switch on dbt Copilot, the Databricks Assistant, or GitHub Copilot / Cursor for the boilerplate — staging models, SQL, PySpark, and DAG scaffolding — and review every line before it merges. The goal is not to write less code; it is to spend your hours on the model and the reliability instead of the syntax.
For the parts that actually pay — data modeling, batch-vs-streaming decisions, cost and architecture — use Claude or ChatGPT as a thought partner (never with real data or credentials). You own the model, the SLAs, and the governance; AI accelerates the building and the analysis around them.
The one rule, forever: Never run AI-generated pipeline code or SQL against production data without review — one wrong join or an unguarded backfill can silently corrupt datasets that thousands of people and downstream models rely on. Test in a dev or staging environment, read the query plan and cost first, and never paste customer PII, credentials, or connection strings into a consumer AI tool. Use enterprise tools with data-retention controls.
The plays — exact steps, exact prompts
Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.
1
Let AI write the pipeline boilerplate so you design the model
Why this pays: Most of a data engineer's day used to be typing near-identical SQL, dbt models, and PySpark. Handing that to an AI assistant lets you ship more pipelines and spend the reclaimed hours on data modeling and architecture — the senior-level work that actually moves your comp toward the top of the band.
Enable an AI assistant in your stack (dbt Copilot, the Databricks Assistant, or Copilot/Cursor in your IDE) and let it scaffold staging models, transformations, and tests from a source schema.
2
Generate a first-draft transformation you then review and harden.
Copy-paste this prompt
Act as a senior analytics engineer. Write a dbt staging model that cleans and standardizes the source table [schema.raw_table] with columns [list columns and types]. Cast types, rename to snake_case, handle nulls sensibly, add a surrogate key, and include column-level dbt tests (not_null, unique, accepted_values where relevant). Explain any assumptions you made so I can verify them.
Review the SQL and the assumptions before you merge — AI guesses at business logic it cannot see.
What you'll havePipelines shipped faster and your hours redirected to modeling and architecture — the senior work that carries a data engineer toward $190,000.
2
Make reliability your edge with AI-generated tests and observability
Why this pays: The difference between a mid-level and a senior data engineer is whether the platform can be trusted at 3 a.m. Using AI to generate thorough data tests and wire up observability makes you the person whose pipelines do not break silently — which is exactly the reputation that earns on-call trust, promotions, and top-of-band pay.
dbt testsGreat ExpectationsMonte CarloClaude
1
Use AI to generate a thorough test suite from a table's schema and known business rules, then add it to dbt or Great Expectations.
2
Draft the expectations you would not have thought to write.
Copy-paste this prompt
Act as a data quality engineer. For a table [name] with columns [list columns, types, and business meaning], propose a complete set of data quality checks: schema/type checks, null and uniqueness constraints, referential integrity, value ranges and accepted values, freshness/volume anomalies, and business-rule assertions. Output them as dbt tests (or Great Expectations expectations) and flag which ones need a human to confirm the thresholds.
Confirm every threshold against real business rules — AI will guess ranges that look plausible but are wrong.
3
Layer an observability tool like Monte Carlo on top for automated freshness, volume, and schema-change alerting so breakages surface before stakeholders notice.
What you'll haveA platform that fails loudly and rarely — the reliability reputation that separates senior data engineers and anchors top-of-band comp.
3
Own the modern warehouse and its AI layer
Why this pays: Companies now expect the data platform to power both dashboards and AI features, and the engineer who owns the warehouse and its semantic and AI layer becomes indispensable. Mastering Snowflake, Databricks, or BigQuery and their AI capabilities is a direct path from pipeline plumber to platform owner — and to the top of the band.
Go deep on one platform (Snowflake, Databricks, or BigQuery) including its AI features — Snowflake Cortex, Databricks Genie — and its governance/catalog layer (Unity Catalog).
2
Design the semantic layer that makes text-to-SQL and self-service safe.
Copy-paste this prompt
Act as a data platform architect. I want to enable trustworthy natural-language querying (text-to-SQL) on our warehouse for [business domain, e.g., revenue analytics]. Design the semantic model I should build: which curated tables/metrics to expose, how to define metrics and joins so the AI answers consistently, the guardrails to prevent wrong or expensive queries, and how to test accuracy before rollout. List the failure modes to watch for.
Curate and test the metrics yourself; a text-to-SQL layer over messy tables produces confident wrong answers.
What you'll haveOwnership of the warehouse and its AI/semantic layer — the platform mandate that turns a data engineer into the person the company cannot ship without.
4
Design data models and architecture with an AI thought partner
Why this pays: Data modeling and architecture — dimensional design, batch vs streaming, build vs buy — are the decisions that define a senior or staff data engineer, and they are the one thing AI cannot own for you. Using AI to structure and stress-test those decisions lets you make them faster and defend them better, which is what earns the staff-level title and pay.
ClaudeChatGPTdbt
1
Use AI to pressure-test a real modeling or architecture decision before you commit the team to it.
Copy-paste this prompt
Act as a principal data engineer and devil's advocate. We need to model [business process, e.g., subscription billing] for analytics. Compare a star-schema dimensional model vs a wide denormalized table vs a data-vault approach for our case: query patterns are [describe], data volume is [describe], and it changes [how often]. Lay out the trade-offs, the grain and key decisions, the strongest case for and against each, and a recommendation with the risks. Challenge my assumptions.
Use it to structure and challenge your thinking; the model and its consequences are yours to own.
2
Turn the output into a short design doc your team reviews. Consistent, well-reasoned data-model decisions are how a data engineer scales judgment beyond their own keyboard.
What you'll haveFaster, better-reasoned, documented modeling and architecture decisions — the staff-level judgment that defines the top of the band.
5
Ship real-time and streaming pipelines
Why this pays: Real-time data — fraud signals, live personalization, operational analytics — commands a premium because far fewer engineers can build it reliably. Adding streaming to your toolkit, with AI helping you through the unfamiliar APIs, is one of the clearest ways to move from commodity batch work into higher-paid territory.
Build a streaming pipeline on Kafka with Flink or Spark Structured Streaming, using AI to explain the APIs and generate the scaffolding as you learn.
2
Get the hard parts — semantics and failure handling — designed correctly.
Copy-paste this prompt
Act as a streaming data architect. I am building a real-time pipeline that ingests [event type] from Kafka and computes [aggregation/join] for [use case]. Walk me through the design decisions: exactly-once vs at-least-once semantics and why, windowing and watermark strategy for late data, state management and checkpointing, backpressure handling, and how to test it. Point out the mistakes that make streaming pipelines silently wrong.
Streaming correctness is subtle — validate the semantics with a controlled test before trusting production numbers.
What you'll haveReliable real-time pipelines in your toolkit — a scarce, premium skill that pushes a data engineer's rate toward the top of the band.
6
Become the platform and AI-data lead
Why this pays: The top of the band belongs to the data engineer who owns the platform, not a corner of it — governance, cost, and the pipelines that now feed AI and ML. Building the data foundation for RAG, features, and analytics, and setting the standards others follow, is what earns the staff/lead title and its comp.
dbtUnity CatalogDagster / Apache Airflow
1
Own the pieces that scale a team: orchestration standards (Airflow/Dagster), a governed catalog (Unity Catalog), transformation standards (dbt), and the pipelines feeding ML and AI (feature and RAG data).
2
Use AI to design the governance and cost framework you will propose to leadership.
Copy-paste this prompt
Act as a data platform lead. Draft a data platform strategy for a [company stage/size] company: our standards for pipeline orchestration and testing, a data governance and cataloging approach, a cost-control plan for the warehouse, and how we will reliably build data pipelines for ML/AI use cases (features and retrieval). Give me a one-page version I can bring to engineering leadership, and flag the decisions that need buy-in.
Adapt to your real stack and constraints; platform strategy is judgment, not a template.
What you'll haveOwnership of the data platform and its AI foundation — the staff/lead mandate that carries a data engineer's comp to the top of the band.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $190,000 tier.
Month 1
Turn on an AI assistant (dbt Copilot, Databricks Assistant, Copilot/Cursor) for boilerplate and review everything you merge.
Months 2-3
Make reliability your edge: generate thorough data tests and wire up observability so pipelines fail loudly, not silently.
Months 3-6
Go deep on one warehouse and its AI/semantic layer; build a governed, self-service metrics foundation.
Months 6-9
Use AI to structure your real data-modeling and architecture decisions as short, defensible design docs.
Months 9-12
Add a scarce, premium skill: ship a reliable real-time/streaming pipeline with AI helping you learn the APIs.
Year 2
Step into the platform + AI-data lead role — owning governance, cost, and the pipelines feeding ML/AI — toward $190,000.
Gear for this job
As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.
Same live O’Reilly Jan 2024 already on data-analyst. This page’s first play is Let AI write the pipeline boilerplate — dbt models and SQL — and Month 1 names dbt Copilot. Not official dbt Labs cert and not CompTIA Data+.
Next steps for a Data Engineer
Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.
Data Engineer work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Database Architects (SOC 15-1243). O*NET Job Zone 4 is typical: a bachelor's degree, so the honest next credential is a professional certificate or bachelor's-level coursework — not a random catalog dump.
The occupation's listed knowledge areas include Engineering and Technology and Design; the links search those subjects, not a generic 'career courses' list.
Data Engineers in this dataset list AJAX among the tools in use, so a program that names that stack is a better fit than a survey course.
Coursera search for engineering and technology — a professional certificate or bachelor's-level coursework that lines up with computing, not a generic professional-development aisle.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Data Engineer work, not a claim that they list a counted SOC 15-1243 inventory.
Write a Data Engineer resume, or one aimed at Computer and Information Systems Managers, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Data Engineer resume that names the actual tasks on this page, or the step-up title Computer and Information Systems Managers, beats a blank template when you apply.
What Data Engineers earn by state
This page does not show a state table, and the reason is worth stating: the Bureau of Labor Statistics does not publish a separate wage series for this job title, so there are no official state figures to show. Scaling the national median by a cost-of-living index would produce a number for every state, but it would be an estimate of living costs wearing a wage’s clothes, and PayCrunch would rather show you nothing than that.
What the national figures say: pay starts near $82,000, the median is $130,000, and the top of the range is $212,260. Those national figures are a PayCrunch estimate, not a Bureau of Labor Statistics published wage for this exact title.
No, but it is transforming the job. AI writes a lot of the SQL, dbt models, and pipeline scaffolding, so the raw coding is being commoditized — which is exactly why the value has moved up to data modeling, reliability, cost, and platform architecture. The engineers who let AI handle the boilerplate and reinvest their time in those higher-order problems are pulling ahead; the ones who define themselves by typing pipelines are the most exposed.
Is it safe to let AI write pipeline code?
Yes, with review. Treat AI-generated SQL and pipeline code as a draft from a fast but context-blind junior: it does not know your business logic, your data's edge cases, or what a bad backfill will cost. Read it, test it in dev or staging, check the query plan and cost, and never point it at production or paste real data and credentials into a consumer tool. The discipline of review is what keeps the speed from becoming an outage.
How does AI actually raise a data engineer's pay?
By freeing you to do the work that is actually scarce. When AI absorbs the boilerplate, your hours go to data modeling, reliability engineering, streaming, and platform ownership — the senior and staff-level skills that companies pay top-of-band rates for. It also lets you ship and own more of the platform than you could by hand. The pay comes from moving up the stack, not from typing faster.
Which warehouse should I specialize in?
Pick the one your target employers actually run — most roles are on Snowflake, Databricks, or BigQuery — and go deep, including its AI and governance features (Cortex, Genie, Unity Catalog). Deep ownership of one modern platform and its semantic/AI layer is far more valuable than shallow familiarity with all three, because it is what turns you from a pipeline builder into the platform owner.
Where should a data engineer start with AI?
Turn on an AI assistant in your warehouse and IDE for boilerplate this week, and immediately reinvest the reclaimed time in data quality — use AI to generate a thorough test suite for your most important tables. Reliability is the fastest way to build a senior reputation, and tests are the highest-leverage place to start. Build from there toward modeling, streaming, and platform ownership.
Methodology & sources
Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.