Role · Data

How to hire a Data Engineer

Data engineers design and maintain the pipelines, warehouses, and ingestion systems that make data usable across the company. They are the reason analysts can run queries and data scientists can train models. Without reliable data infrastructure, every dashboard is suspect and every model is fragile.

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Why this role is hard to hire

The hiring challenge

Data engineering candidates come with wildly different depth despite similar-looking resumes. Some have built real pipelines that handle failures gracefully; others have only written SQL queries in a notebook. The real test is whether they can design a pipeline that handles messy, real-world data — late-arriving records, schema changes, duplicate events — not just write a clean query on a clean table. Hands-on experience with orchestration tools and warehouse cost management matters far more than SQL speed.

What to look for in a Data Engineer

Four traits matter: Pipeline thinking (do they design for failure — retries, idempotency, alerting — or only for the happy path?). Data modelling discipline (can they explain why they chose a star schema versus a flat table, and what the trade-offs are?). Cost awareness (do they think about warehouse compute costs before writing a query that scans terabytes?). Collaboration with downstream users (can they understand what an analyst or data scientist actually needs, not just what the ticket says?).

For Indian companies, also look for experience with the cloud platforms your team uses (AWS, GCP, or Azure — do not assume they are interchangeable), comfort with Indian data quirks (PAN numbers, GST data, regional date formats), and willingness to document their work so the next engineer can maintain it.

Common mistakes when hiring Data Engineers

Testing SQL speed instead of pipeline design. A candidate who writes a fast query may not know how to build a pipeline that runs reliably every day. Give them a pipeline design problem, not just a SQL quiz.

Ignoring failure handling. Ask what happens when their pipeline fails at 3 AM. If they have never thought about retries, alerting, or recovery, they have only built toy pipelines.

Not checking cost awareness. A data engineer who does not think about warehouse costs will run up large bills without realising it. Ask them about a time they optimised a query or pipeline for cost.

What to test

Key skills for a Data Engineer

SQL (advanced joins, window functions, optimisation)
Pipeline orchestration (Airflow, dbt, or similar)
Data modelling (star schema, dimensional)
Warehouse design (Snowflake, BigQuery, Redshift)
Cost awareness and query optimisation
Failure handling (retries, idempotency, alerting)
Collaboration with analysts and data scientists
Documentation discipline

Sample questions

What a great interview looks like

Coding

"Write a SQL query that handles slowly changing dimensions (Type 2) correctly."

Voice

"Walk me through a pipeline you designed. What failed, and how did you make it reliable?"

Scenario

"Warehouse costs tripled last quarter. Rank investigation steps."

Voice

"How do you decide between processing data in batch versus real-time? Give me a specific example."

Scenario

"An analyst reports that a dashboard is showing stale data. Walk me through how you would investigate."

Every question is from the GoodFit library. Customize the rubric for your context in the platform.

Suggested format

Recommended interview process

Round 1: AI Voice Interview

15 min

Pipeline design walkthrough, failure handling reasoning, and collaboration style assessment.

Round 2: Technical Assessment

60 min

SQL challenge on realistic data plus a pipeline design exercise. Graded on modelling, failure handling, and cost reasoning.

Round 3: Engineering Manager Interview

45 min

Architecture discussion, team fit, and documentation habits. Only candidates who cleared Rounds 1-2.

Want to set up this interview process for your Data Engineer openings? GoodFit handles Rounds 1 and 2 automatically. Your team only steps in for the final conversation.

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Ready-made template