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Data Engineer

Data Engineer

Data Engineers at high-growth companies earn $106K–$171K. Median: $142K. Based on 232 public job postings (2025–2026). US DOL certified wage filings (FY2026): $152K median (▲ +18% since 2020). Recruiting from Scratch's network includes 34,700+ data engineers, including 2,900+ with top-tier-company experience.

💰 $106K–$171K salary range

Median: $142K  ·  Based on 232 public job postings  ·  Updated August 30, 2026


What is a Data Engineer?

A data engineer builds and maintains the pipelines, warehouses, and infrastructure that move data from where it's generated to where it's useful. They're the foundation of a company's analytics capability — without reliable data pipelines, data scientists can't analyze, analysts can't report, and machine learning engineers can't train. At a startup, data engineers often own the entire data stack: ingestion, transformation, warehousing, and observability.

At what stage should you hire a Data Engineer?

Series A through Series B, once your product is generating enough event data that analytics is a meaningful function and the current ad hoc approach (manual SQL pulls, brittle scripts) is slowing the team down. The prerequisite: you have a destination for the data (Snowflake, BigQuery, Redshift) and a business reason to query it reliably. Without either, you're building infrastructure for its own sake.

Common titles for this role

  • Data Engineer
  • Analytics Engineer (more transformation-focused)
  • Data Infrastructure Engineer
  • Senior Data Engineer
  • Data Platform Engineer
  • ETL Engineer

What does a Data Engineer do at a startup?

  • Build and maintain data ingestion pipelines: Fivetran, Airbyte, custom connectors, Kafka
  • Design and maintain the data warehouse schema: Snowflake, BigQuery, or Redshift
  • Build transformation pipelines using dbt or equivalent tools
  • Implement data quality monitoring and alerting
  • Instrument product event tracking: Segment, Amplitude, or custom event pipelines
  • Enable self-serve analytics: clean, documented, queryable data models
  • Manage data infrastructure costs and optimize query performance

Key skills and qualifications

  • Strong SQL expertise — this is the core language of data engineering
  • Python for pipeline development and automation
  • Experience with data warehouse platforms: Snowflake, BigQuery, or Redshift
  • Pipeline orchestration: Airflow, Prefect, Dagster, or dbt Cloud
  • Data ingestion tools: Fivetran, Airbyte, or custom connector development
  • Understanding of streaming vs. batch processing tradeoffs

Why hire your Data Engineer through Recruiting from Scratch?

  • Data engineering is a specialized search — we screen for hands-on pipeline and warehouse experience, not just SQL familiarity
  • 29-day average time to hire — data engineering searches benefit from a pre-vetted candidate pool
  • 300+ placements at VC-backed companies across data and engineering functions
  • Pre-vetted for the modern data stack: dbt, Snowflake, Airflow experience confirmed before you see a resume
  • No upfront fees

Frequently Asked Questions: Data Engineer

What does a Data Engineer earn?

Based on 1237 real postings in our database, the median salary for a Data Engineer is $168K. The typical range for this role falls between $133K and $200K. These figures reflect current market demand and the specialized skills required for data engineering roles.

How long does it take to hire a Data Engineer?

Hiring a Data Engineer can be a competitive process, but our recruiting firm significantly streamlines it. We typically place Data Engineers within an average of 29 days. This is considerably faster than the industry average of 45-60 days, thanks to our extensive network of over 900K professionals and efficient matching process.

What should you look for when hiring a Data Engineer?

When hiring a Data Engineer, prioritize strong foundational skills in data modeling, ETL processes, and database management. Look for candidates proficient in programming languages like Python or Java, and experienced with big data technologies such as Spark or Hadoop. A solid understanding of cloud platforms like AWS, Azure, or GCP is also crucial for modern data infrastructure.

How do you assess a Data Engineer candidate effectively?

To effectively assess a Data Engineer candidate, combine technical interviews with practical problem-solving exercises. Ask about their experience designing and maintaining data pipelines, and present them with real-world data challenges to solve. Our firm often uses structured technical assessments and behavioral questions to evaluate both their technical depth and their approach to complex data problems.

Is a Data Engineer role typically remote or in-person?

The Data Engineer role has seen a significant shift towards remote work, especially in recent years. While some companies prefer in-person or hybrid models, many organizations now offer fully remote positions to access a wider talent pool. We find that offering flexibility can greatly improve your chances of securing top-tier Data Engineering talent from our network of over 900K professionals.

📊 Salary breakdown

Data Engineer salary by location

  • All locations: $142K median ($106K–$171K typical range)
  • New York: $164K median, $150K–$212K range (+16% vs. national)
  • San Francisco: $215K median, $165K–$245K range (+51% vs. national)
  • Chicago: $150K median, $146K–$165K range (+6% vs. national)
  • Atlanta: $104K median, $101K–$134K range (-27% vs. national)
  • Washington DC: $93K median, $86K–$111K range (-35% vs. national)
  • San Diego: $98K median, $98K–$128K range (-31% vs. national)
  • Boston: $135K median, $135K–$182K range (-5% vs. national)
  • Austin: $120K median, $120K–$120K range (-16% vs. national)

Highest paying companies

San Francisco

  • Lambda: $286K median

New York

  • Fanatics Collectibles: $163K median
  • Spotify: $162K median
  • Oscar Health: $138K median

Based on companies with 3+ active postings. Median of publicly advertised salary range.

What's typically included

  • Health insurance: 69% of postings
  • Equity (RSU/options): 61% of postings
  • Flexible PTO: 57% of postings
  • Parental leave: 30% of postings
  • Learning/education budget: 15% of postings
  • Visa sponsorship: 2% of postings

Typical experience required: 4–5 years.

Data salaries by seniority

What does a Data Engineer do?

Data Engineers are data professionals who build the pipelines, models, and analytics the business runs on. This benchmark reflects Mid-level base compensation at high-growth and AI-native companies.

Government wage data

For corroboration, U.S. Department of Labor certified wage filings (FY2026) show a median of $152K for Data Engineer roles, ranging $130K–$176K, across 2,683 filings. This is reported separately from — never blended with — the job-posting median above.

Common questions

What is the average Data Engineer salary at an AI startup?
The median pay is $142K, with a typical range of $106K–$171K, based on 232 public job postings collected in 2025–2026.

Where does this salary data come from?
It is aggregated from public job postings on company career pages — no private placement or client data. We require a minimum of 15 postings per role.

Methodology

Figures are the midpoint of each posting's advertised USD salary range, aggregated from 232 public job postings (2025–2026). The range shown is the 25th–75th percentile; the median is the 50th percentile. Equity is separate and not included; where a posting's range reflects on-target earnings (OTE) for commission roles, that is included in the midpoint. Roles require at least 15 postings to be published. Last refreshed August 30, 2026.

📖 Related guides

Hiring Data Engineers? RFS recruiters specialize in Data placements at AI-native and high-growth startups. Talk to an RFS recruiter →

Are you a Data Engineer? See open Data Engineer roles →

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