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Data Engineers at high-growth companies earn $137K–$215K. Median: $176K. Based on 101 public job postings (2025–2026).
Median: $176K · Based on 101 public job postings · Updated April 19, 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 317 real postings in our database, a Data Engineer typically earns a median salary of $185K, with a common range between $155K and $215K. These figures reflect current market demand and the specialized skills required for the role.
Our average time to placement for a Data Engineer is 29 days, significantly faster than the industry average of 45-60 days. We achieve this efficiency through our extensive network of over 900K professionals and a refined screening process, ensuring you connect with top talent quickly.
When hiring a Data Engineer, we advise focusing on strong foundational skills in data modeling, ETL processes, and proficiency with cloud platforms like AWS, Azure, or GCP. Look for candidates who demonstrate a deep understanding of data warehousing principles and can design robust data architectures. Problem-solving ability and a collaborative mindset are also crucial for success in this role.
To effectively assess a Data Engineer candidate, we recommend a multi-stage approach including technical interviews focusing on SQL, Python, and data structure concepts. Practical coding challenges that simulate real-world data pipeline problems are invaluable. System design questions can reveal their architectural thinking, while behavioral questions help gauge their fit within your team and company culture.
The Data Engineer role has seen a significant shift towards remote or hybrid work models, though in-person opportunities still exist depending on company culture and project requirements. Many organizations find that offering flexibility expands their talent pool considerably. We observe a strong preference for remote options among our network of over 900K professionals, making it a key consideration for attracting top candidates.
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