Compensation
Market data- p25
- $69,000
- median
- $145,000
- p75
- $196,000
Annual base, USD, from public postings · based on n=261 · Updated November 2026. How we calculate this
Career & pay · engineering
Data Engineer
Market data for this role — salary, demand, and skills. See live openings below.
Data engineers command a median salary of $137,919 with strong hiring demand across 227 active postings. The role sits at the intersection of software engineering and data infrastructure, requiring both technical depth and business acumen to build systems that power analytics and machine learning.
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About the role
What a Data Engineer does
What data engineers actually do
Data engineers build and maintain the infrastructure that lets organizations collect, store, process, and analyze data at scale. Unlike data scientists who ask questions of data, or analysts who report on it, data engineers are responsible for the plumbing—the pipelines, warehouses, and systems that make data accessible and reliable in the first place.
Day-to-day work varies by company size and maturity. At a startup, you might design a data warehouse from scratch, select tools, and build initial ETL (extract, transform, load) pipelines. At a larger organization, you're likely optimizing existing systems, debugging data quality issues, managing data governance, and collaborating with data scientists and analysts who depend on your infrastructure. You write code—usually Python, SQL, Scala, or Java—but you're also making architectural decisions about how data flows through systems.
The role requires comfort with ambiguity. You need to understand what business questions the organization is trying to answer, then work backward to design systems that support those questions reliably and cost-effectively. A poorly designed pipeline doesn't just slow down analytics; it can corrupt decision-making across the entire company.
The compensation picture
Data engineers earn a median of **$137,919** annually in the current market. The 25th percentile sits at **$95,000**, while the 75th percentile reaches **$170,750**. This range reflects real variation based on experience, location, company size, and specialization.
Entry-level data engineers (0–2 years) typically land in the lower half of that range, often paired with equity at startups. Mid-level engineers (3–7 years) cluster around the median, especially in tech hubs. Senior engineers and those with specialized expertise in high-demand areas—real-time streaming, data governance, or cloud architecture—push toward and beyond the 75th percentile.
The spread also reflects geography. Remote-first companies and those in lower cost-of-living areas may offer closer to the p25 figure, while San Francisco, New York, and Seattle command premiums. Equity and bonus structures vary significantly; some offers are heavily cash-weighted, others front-load equity.
Hiring demand and market dynamics
There are currently **227 active postings** across **133 companies** hiring for data engineer roles. That's meaningful demand—enough to suggest the role isn't a fad, but not so saturated that every qualified candidate is competing with thousands of others.
The distribution across 133 companies tells you something important: data engineering isn't concentrated in a handful of mega-tech firms. It's distributed across finance, healthcare, e-commerce, media, logistics, and dozens of other industries. Every company that collects data needs someone to manage it.
This demand is relatively stable. Data infrastructure is a long-term investment for companies; they don't hire data engineers for a quarter and then stop. Once a company reaches a certain scale—usually when they're collecting more data than a single analyst can handle in spreadsheets—they need engineering talent to build systems. That creates a floor under demand.
Skills that actually matter
The core technical skills are non-negotiable:
**SQL** is table stakes. You need to write complex queries, understand query optimization, and think in terms of relational data. If you can't write SQL fluently, you're not ready for this role.
**Python** or **Scala** for data processing. Python dominates in most organizations because it's readable and has a rich ecosystem (pandas, PySpark, Airflow). Scala is common in companies using Spark at scale. Java appears in legacy systems and some financial institutions.
**Cloud platforms**—AWS, GCP, or Azure. Most companies have migrated or are migrating to cloud. You need to understand cloud storage (S3, GCS, Blob Storage), compute services (EC2, Compute Engine), and managed data services (Redshift, BigQuery, Synapse). Pick one and go deep; the concepts transfer.
**Data warehousing and pipeline tools**. Snowflake, Redshift, BigQuery, and Databricks are the current standard. For orchestration, Airflow is ubiquitous; dbt is increasingly expected for transformation logic. Kafka or Pub/Sub for streaming. You don't need to know all of them, but you need to understand the category and be able to learn new tools quickly.
Beyond the technical stack, the skills that separate good data engineers from great ones:
**Systems thinking**. You need to understand how data flows end-to-end, where bottlenecks appear, and how to design for reliability and cost. This comes from experience and from reading architecture docs and asking questions.
**Communication**. Data engineers sit between software engineers, data scientists, analysts, and business stakeholders. You need to explain trade-offs in terms non-engineers understand, and translate business requirements into technical specs.
**Debugging and troubleshooting**. Pipelines fail. Data quality issues surface at 2 a.m. You need to be comfortable diving into logs, understanding what went wrong, and fixing it without panic.
**Pragmatism**. The perfect architecture that takes six months to build is worse than the 80% solution that works in six weeks. You need to know when to optimize and when to ship.
How to get hired
If you're early in your career, the path is usually: strong fundamentals in SQL and Python, a portfolio project or two that show you can build a real pipeline (even a small one), and ideally some internship or junior role experience. Many companies will hire junior data engineers and train on their specific stack.
If you're transitioning from software engineering, you have an advantage in coding ability and systems thinking; focus on learning SQL and data-specific tools. If you're coming from analytics or data science, you likely have domain knowledge and SQL skills; level up on software engineering practices (version control, testing, CI/CD).
In interviews, expect:
- **SQL and coding questions** on data processing and algorithm problems. These are usually straightforward; they're testing whether you can think clearly and code without errors. - **System design questions** about how you'd build a data pipeline for a specific use case. Walk through your thinking: what tools would you use, how would you handle scale, what about failure modes? - **Behavioral questions** about how you've debugged a production issue, worked with stakeholders, or handled ambiguous requirements. - **Take-home projects** that ask you to build a small pipeline or analyze a dataset. These are often more predictive than whiteboard questions.
When evaluating offers, look beyond base salary. Equity matters at startups; bonus structure matters at larger companies. Consider the tech stack (are they using tools you want to learn?), the team size and maturity, and whether you'll have mentorship or be the only data engineer.
How to hire well
If you're hiring, the market is competitive but not impossible. With 227 postings across 133 companies, you're competing for talent, but there's enough supply that a thoughtful hiring process can work.
Be specific about your stack and what you actually need. "Data engineer" means different things at different companies. Do you need someone to build real-time streaming pipelines, or are you focused on batch ETL? Do you need deep cloud expertise, or are you on-premise? The more specific you are, the easier it is for candidates to self-select and for you to find the right fit.
Test for fundamentals, not trivia. SQL and Python questions should be realistic, not gotchas. System design questions should be about problems you actually solve. If you ask about a tool you use, be prepared for candidates who haven't used it but can learn it.
Value communication and curiosity over perfect credentials. A data engineer who asks good questions and explains their thinking clearly is more valuable than someone who knows every tool but can't collaborate.
Offer competitive compensation. The median is $137,919; if you're offering significantly below that, you'll struggle to attract experienced talent. If you're a startup, equity can close the gap, but be transparent about it.
The long-term outlook
Data engineering is not a temporary role. As long as companies collect data, they need engineers to manage it. The tools will change—we'll move from Airflow to something else, from Snowflake to something new—but the fundamental need is durable.
The role is also evolving. More companies are adopting data mesh architectures, where data engineering responsibilities are distributed. Real-time data is becoming more common. Data governance and privacy are increasingly critical. These trends create new specializations and opportunities for engineers who stay current.
For candidates, this is a role with staying power and room to grow. For companies, it's a role that's hard to hire for but essential to get right.
By seniority · global median
Intern$37,036
Junior$60,183+$23,147
Mid$137,959+$77,776
Senior$135,200
Staff$157,401+$22,201
Principal$194,437+$37,036
Director$222,214+$27,777
VP$277,767+$55,553
C-level$370,356+$92,589
Where would you land?
~50th percentile — about $145,000 would out-earn an estimated 50% of Data Engineers.
Estimated from the p25–p75 band. For orientation, not an offer.
Open positions
Live Data Engineer roles
The most recent openings tracked across the market. Apply directly — no middle layer.
- Data EngineerNASCARDaytona Beach, Volusia Countytoday
- AWS Data EngineerLynx Recruitment LtdVictoria, South West Londontoday
- Senior Data EngineerSoFiCottonwood Heights, Utah, United Statesyesterday
- Databricks Data Engineer (m/w/d)DevoteamFrankfurt am Main, HE, DEyesterday
- Databricks Data Engineer (m/w/d)DevoteamMünchen, BY, DEyesterday
- Data Engineer, Amazon PrimeAmazon Inc.Greater Vancouver, British Columbiayesterday
- Data Engineer (Databricks)Syntax Consultancy LtdPride Park, Derbyyesterday
- Senior Analytics Engineer, FinanceNVIDIAUS, CA, Santa Clara2d ago
- Senior BI Analytics EngineerCarwowLondon2d ago
- Energy Analytics Manager, Clean EnergyMetaPierre, Hughes County2d ago
- Energy Analytics Manager, Clean EnergyMetaProvidence, Providence County2d ago
- Analytics EngineerRedditRemote - United StatesRemote2d ago
Openings elsewhere
Live Data Engineer openings
Roles from other companies hiring Data Engineers right now, sourced straight from their own job boards.
- Senior Data EngineerSoFiCottonwood Heights, Utah, United States
- Databricks Data Engineer (m/w/d)DevoteamFrankfurt am Main, HE, DE
- Databricks Data Engineer (m/w/d)DevoteamMünchen, BY, DE
- Senior Analytics Engineer, FinanceNVIDIAUS, CA, Santa Clara
- Analytics EngineerRedditRemote - United States
- Data EngineerBosch GroupMaklár, HU
- Data Foundation Solution EngineerSalesforce3 Locations
- Data Engineer, ProductNotionSan Francisco, California
- Senior Data EngineerCloudflareIn-Office
- Senior Data Engineer Snowflake F/HDevoteamLyon, Auvergne-Rhône-Alpes, FR
Adjacent paths
Related roles
Software Engineer2,536 openView market data →Senior Software Engineer2,086 openView market data →Staff Software Engineer751 openView market data →Machine Learning Engineer647 openView market data →DevOps Engineer575 openView market data →Engineering Manager506 openView market data →Fullstack Engineer438 openView market data →Principal Software Engineer331 openView market data →
Where it pays
Compensation by region
Median annual base in USD by region. Hiring globally widens the band you can recruit from — and the arbitrage you can capture.
Regionp25Medianp75Source
US$100,000$140,000$140,000Market data
CA$92,192$108,461$127,984Estimated
GB$87,695$103,171$121,741Estimated
UY$49,469$58,199$68,675Estimated
MX$47,220$55,553$65,553Estimated
CR$44,972$52,908$62,431Estimated
CL$44,972$52,908$62,431Estimated
BR$44,972$52,908$62,431Estimated
AR$38,226$44,972$53,067Estimated
CO$38,226$44,972$53,067Estimated
PE$35,977$42,326$49,945Estimated
Good to know
Frequently asked
What's the salary range for a Data Engineer?
The median Data Engineer salary is $145,000. Most Data Engineer roles pay between $69,000 and $196,000 (USD, annual). Based on 261 salary observations.
Which companies hire Data Engineers?
Companies actively hiring Data Engineers include Samsara, Devoteam, anthropic, Supabase, Prolific, Improving.
How do I get hired as a Data Engineer on diiirect?
Apply directly to 269 open Data Engineer postings on diiirect — no recruiters, no middle layer. Companies review your profile and hire you directly.
Hiring kit
Templates for hiring Data Engineers
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Salary and demand figures are derived from public job postings and the BLS OEWS baseline. Read how we calculate this.