Data Engineer Salary in 2026: 6 Cities Compared (With Real Numbers)

Data engineering is the unsexy infrastructure work that makes everything else in the data/AI world possible. Every machine learning model needs clean training data. Every analytics dashboard needs a reliable pipeline. Every real-time recommendation needs streaming infrastructure. Data engineers build all of it.

The AI boom has made data engineers more valuable than ever — and salaries reflect it. San Francisco's ceiling hits $300K for senior data engineers at AI companies. Meanwhile, Berlin pays €61K–€80K for similar work. Same Spark jobs, same Airflow DAGs, same dbt models, wildly different compensation.

Here's what data engineers actually earn across 6 major markets in 2026.

The Quick Comparison

The standout number: San Francisco's $300K ceiling. That's not total comp — that's base salary at top-tier AI companies where data infrastructure is the competitive advantage. The AI boom hasn't just lifted ML engineer salaries; it's pulled data engineers up with them.

San Francisco: $107K–$300K

San Francisco's data engineering market is driven by one thing: AI companies need data infrastructure at an unprecedented scale.

OpenAI, Anthropic, Google DeepMind, and hundreds of AI startups all need engineers who can build the pipelines that feed training data to models. These aren't standard ETL jobs — they're petabyte-scale data systems processing billions of records with strict quality, freshness, and compliance requirements. The $300K ceiling exists because this infrastructure is directly tied to model quality, which is directly tied to company survival.

Beyond AI, the traditional data engineering market in SF remains strong. Databricks (headquartered in SF) is literally building the tools that other data engineers use. Snowflake, Confluent, and dbt Labs all have major SF presences. Working at a data tooling company as a data engineer is a career accelerator — you understand the product deeply and your next job values that expertise.

The $107K floor represents early-career data engineers at smaller companies. Even at this level, you're likely working with production data pipelines — not toy datasets. The learning curve is steep, but the floor is higher than most cities' medians.

Full breakdown: Data Engineer Salary in San Francisco

Remote (US): $64K–$275K

Remote data engineering has the second-widest range on this list — $211K between floor and ceiling — and the second-highest ceiling at $275K. Data engineering translates to remote work naturally: pipelines run in the cloud, monitoring is dashboard-based, and collaboration happens through code reviews and documentation.

The $275K ceiling represents senior data engineers at remote-first companies or AI companies with distributed teams. These roles involve architecting company-wide data platforms, managing petabyte-scale data lakes, or building real-time streaming infrastructure — work that requires deep expertise and commands top-tier compensation regardless of location.

The $64K floor is notable — it's the lowest on this list for US data engineers. It reflects roles at bootstrapped companies or positions with aggressive location-based pay adjustments. A data engineer in rural Arkansas working for a company that adjusts pay to local cost of living might see this number.

The sweet spot for remote data engineers is the $130K–$180K range, where most experienced engineers with 3-5 years of pipeline-building experience land. At this level, the location arbitrage is enormous: $155K while living in a city with a $50K median household income delivers extraordinary quality of life.

Current ranges: Data Engineer Salary — Remote

New York: $84K–$245K

New York's data engineering market is shaped by the intersection of finance and tech — and finance generates more data per dollar of revenue than almost any other industry.

Banks, hedge funds, and trading firms need data engineers for market data pipelines, risk analytics infrastructure, trade surveillance systems, and regulatory reporting. Goldman Sachs, JPMorgan, and Bloomberg all maintain large data engineering teams. Bloomberg's data infrastructure is particularly notable — Bloomberg Terminal serves real-time financial data to hundreds of thousands of professionals, and the data engineering behind it is world-class.

The $245K ceiling reflects senior roles at finance or Big Tech (Google, Amazon, Meta all have substantial NYC data engineering teams). Total compensation at quantitative trading firms can exceed this significantly when bonuses are included.

The $84K floor is lower than expected for NYC. It reflects the breadth of the city's economy — media companies, non-profits, government agencies, and small businesses all hire data engineers, but many can't compete with tech/finance compensation.

The $121K median sits in an awkward spot for NYC's cost of living. Combined with high state and city taxes, a data engineer earning the median in Manhattan needs to be strategic about housing. Many NYC data engineers live in Brooklyn or Jersey City, where rent is 20–30% lower, to make the numbers work.

See the numbers: Data Engineer Salary in New York

Seattle: $81K–$200K

Seattle's data engineering market is dominated by one employer: Amazon. And within Amazon, data engineering spans an astonishing range of roles — from processing retail purchase data to building AWS data services (Redshift, Glue, Lake Formation, Kinesis) used by millions of customers.

Working on AWS data services as a data engineer is a unique career position: you're building the tools that define the industry. The experience translates directly to high-paying roles at any company that uses these services — which is most companies.

Microsoft's Azure data platform team (Azure Data Factory, Synapse Analytics, Fabric) provides similar opportunities. Both companies pay $150K–$200K for senior data engineers, with equity pushing total comp significantly higher.

The $81K floor reflects data engineering roles at non-tech companies, agencies, or smaller startups in the Seattle metro. The gap between Big Tech data engineering ($150K+) and the local market ($81K–$120K) is stark.

Washington's no state income tax makes the math favorable. A data engineer earning $140K in Seattle takes home roughly $15K more annually than the same salary in San Francisco or New York.

Full data: Data Engineer Salary in Seattle

London: £47K–£121K

London's data engineering market benefits from the city's dual identity as a financial center and a tech hub. The demand for data engineers comes from both traditional finance (banks processing transaction data, regulatory reporting) and modern tech (fintech startups building data-driven products).

The £121K ceiling represents senior data engineers at Big Tech (Google, Amazon, Meta London offices) or top-tier fintech companies. Revolut's data infrastructure — processing millions of transactions across 30+ currencies in real-time — is one of the most technically interesting data engineering challenges in Europe. Senior data engineers at companies solving these problems earn at or near the ceiling.

The £47K floor represents junior or mid-level roles at smaller companies, agencies, or enterprises early in their data modernization journey. Many UK companies are still migrating from on-premise data warehouses to cloud-based architectures, creating demand for data engineers at all experience levels.

The £80K median is comfortable in London — though housing takes a larger share of income than in most other cities on this list. Zone 2 one-bedroom apartments run ~£1,500–£1,800/month.

Full data: Data Engineer Salary in London

Berlin: €61K–€80K

Berlin has the tightest salary range on this list — just €19K between floor and ceiling. This compression is characteristic of the German tech market: less variance, more predictability, and salaries that look modest until you factor in what's included.

A data engineer earning €71K (median) in Berlin takes home ~€3,600/month after taxes, healthcare, pension contributions, and unemployment insurance. Rent for a one-bedroom in Friedrichshain: ~€1,000. Public transit: €49/month. No car needed, no health insurance bill, no retirement savings anxiety (mandatory pension).

The employer landscape includes Delivery Hero (restaurant logistics data), Zalando (e-commerce recommendation pipelines), HelloFresh (supply chain data), and a growing number of data-focused startups. Amazon's Berlin office has expanded its data engineering team significantly.

The €80K ceiling is achievable at senior levels, but breaking above it in Berlin typically requires joining a US company with a Berlin office that pays at US-adjacent rates. These hybrid-pay roles are the best-kept secret of the Berlin data engineering market.

German labor protections add invisible value: termination requires cause and 3+ months notice for senior employees, 30 days paid vacation is standard, and overtime is culturally discouraged. For data engineers coming from US companies that expect 24/7 on-call and weekend deployments, Berlin's work culture is a genuine lifestyle upgrade.

See the details: Data Engineer Salary in Berlin

Data Engineer vs. Data Scientist: The Salary Comparison

Data engineers and data scientists work on the same datasets but do fundamentally different things — and the salary differences reflect this.

In San Francisco, data engineers earn more than data scientists — a reversal from five years ago. The reason: AI companies need infrastructure more than analysis. Building the pipeline that feeds training data to GPT-5 is harder to hire for than analyzing the output.

In New York and Berlin, the relationship is roughly at parity, with data scientists slightly ahead. These markets have more analytics-focused data demand (finance, e-commerce) where the scientist's statistical expertise is the bottleneck, not the infrastructure.

What Drives Data Engineer Salaries Higher?

Three specializations push data engineers toward the top of every market's range:

1. Real-time streaming. Batch processing (Spark, dbt, Airflow) is table stakes. Engineers who can build and maintain real-time streaming systems (Kafka, Flink, Spark Streaming) for use cases like fraud detection, real-time pricing, or live dashboards earn 20–30% premiums. Real-time is harder to debug, harder to maintain, and harder to hire for. 2. AI/ML data infrastructure. Building feature stores, training data pipelines, data quality frameworks for ML models, and vector database infrastructure. As AI becomes central to every company's strategy, the engineers who feed the models become increasingly valuable. 3. Data platform architecture. Designing company-wide data platforms — choosing the warehouse (Snowflake vs. BigQuery vs. Redshift), building the orchestration layer, implementing data governance, managing costs. This requires both deep technical knowledge and the ability to make decisions that affect dozens of teams.

The Data Engineering Career Ladder

The junior-to-mid transition is where you go from running existing pipelines to building new ones. The mid-to-senior transition is where you start designing data architectures and mentoring others. Each jump brings 30–50% salary increases in strong markets.

The Bottom Line

Data engineering in 2026 is riding the AI wave — every model needs data, and someone has to build the systems that deliver it. The role has evolved from "ETL developer" to a legitimate engineering discipline with its own tools, frameworks, and career ladder.

San Francisco leads on both median ($171K) and ceiling ($300K), driven by AI company demand. Remote roles offer the best arbitrage ($155K median from anywhere). Berlin provides the best lifestyle-per-euro with tight but comfortable salary bands.

The biggest salary lever is specialization. Batch ETL engineers earn market rate. Real-time streaming specialists and AI data infrastructure architects earn premiums in every market. The tools are learnable — Kafka, Flink, feature stores — but the experience takes years to build. Start specializing early.