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Data Engineers at high-growth companies earn $115K–$180K. Median: $145K. Based on 224 public job postings (2025–2026).
Median: $145K · Based on 224 public job postings · Updated July 1, 2026
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.
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.
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.
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.
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.
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.
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.
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