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How to hire a Data Engineer

Data engineers build 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 solid data infrastructure, nobody can trust a dashboard and every model breaks easily.

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Data Engineer

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What is a Data Engineer?

A Data Engineer builds the pipes that move data from where it is made to where people can use it. They keep it accurate along the way. That means pulling data in from apps, outside systems, and event streams. It means turning it into models analysts can query without knowing the source systems. It means scheduling jobs so they run in the right order and recover when they fail. It also means tests and monitoring that catch bad data before a dashboard shows it as fact.

Also called
Analytics Engineer, ETL Developer, Data Platform Engineer
Function
Data and analytics engineering
Level
Mid to senior individual contributor
Reports to
Head of Data, Data Engineering Manager, or CTO
Common stack
SQL and Python; Snowflake, BigQuery or Redshift; dbt; Airflow; Spark; Kafka
Next step
Senior or Staff Data Engineer, Data Platform Lead, or Head of Data

The hiring challenge

Data engineering candidates have very different depth, even when their resumes look alike. Some have built real pipelines that recover from failures. Others have only written SQL queries in a notebook. The real test is whether they can design a pipeline for messy, real-world data. Think late records, schema changes, and duplicate events. A clean query on a clean table proves little. Hands-on work with orchestration tools and warehouse costs matters far more than SQL speed.

What to look for in a Data Engineer

Four traits matter. Pipeline thinking: do they plan for failure with retries, idempotency, and alerts? Or only for the happy path? Data modeling discipline: can they explain why they chose a star schema over a flat table, and the trade-offs? Cost awareness: do they think about warehouse compute costs before writing a query that scans terabytes? Working with the people who use the data: do they know what an analyst or data scientist really needs? Do they look past what the ticket says?

Also look for experience with the cloud platform your team uses. AWS, GCP, and Azure are not the same, so do not assume they are. Look for comfort with local data quirks, like tax IDs, address formats, and regional date formats. And look for a habit of writing docs so the next engineer can maintain their work.

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 every day without breaking. Give them a pipeline design problem as well as a SQL quiz.

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

Not checking cost awareness. A data engineer who ignores warehouse costs will run up large bills without noticing. Ask about a time they made a query or pipeline cheaper to run.

Key skills for a Data Engineer

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

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. What would you check first, second, and third?"

Voice

"How do you choose between batch and real-time processing? Give me a specific example."

Scenario

"An analyst says a dashboard is showing old data. How would you find out why?"

You can use these questions in Goodfit as they are, or edit the questions and the scoring for your team.

Recommended interview process

  1. 1

    Round 1: AI Voice Interview15 to 20 min

    The candidate walks through a pipeline design, explains how they handle failures, and talks about how they work with others.

  2. 2

    Round 2: Technical Assessment60 min

    A SQL challenge on real-looking data plus a pipeline design exercise. Scored on modeling, failure handling, and cost reasoning.

  3. 3

    Round 3: Engineering Manager Interview45 min

    Talk through architecture, team fit, and how they document their work. Only for candidates who passed Rounds 1-2.

In Goodfit, the AI interview runs the first round for every applicant. Skill tests, coding tests and roleplays can run in Goodfit too. Your team reads the scorecards and joins for the later conversations.

FAQ

Data Engineer hiring questions

What does a data engineer do?
They build and run data pipelines. They pull data from source systems and APIs and load it into a warehouse or lake. They turn raw records into clean, documented models. They schedule those jobs and keep them in order. They watch for late data, odd volumes, and schema changes that would quietly break things. They also own cost, since careless queries drive up warehouse bills. And they own access control, since the warehouse often holds the most sensitive data a company has.
What is the difference between a data engineer, a data analyst, and a data scientist?
The data engineer builds and maintains the pipelines and models that make data usable. The data analyst uses that data to answer business questions and build reports. The data scientist uses statistics and machine learning to predict or explain what will happen. Analysts and scientists depend on data engineering, never the other way around. Their work is only as good as the data under it. That is why too few data engineers slowly hurts everything built on top.
What is the difference between ETL and ELT?
ETL pulls data out, transforms it outside the warehouse, then loads the finished result. ELT loads raw data first and transforms it inside the warehouse with SQL. ELT is now the default. Cloud warehouses are cheap and fast enough to transform large amounts of data. Keeping the raw data also means you can fix a mistake without pulling from the source again. ETL still makes sense when data must be cleaned or masked before it lands anywhere.
What skills does a data engineer need?
They need SQL well beyond basic queries: window functions, query plans, and why a join is slow. They need Python for orchestration and for anything SQL cannot do. They need dimensional modeling, so analysts do not rebuild business logic in every report. They need an orchestrator such as Airflow and the habits that go with it. That means idempotent jobs, sensible retries, and backfills that do not double-count. They need data quality testing and warehouse cost awareness. They also need enough streaming knowledge to know when batch is enough, which is most of the time.
What is an analytics engineer and how is it different?
An analytics engineer sits between data engineering and analysis. They own the transformation layer, usually in dbt, where raw tables become documented, tested models. They write SQL more than infrastructure code. They are closer to the business questions. The split works because the skills really are different. Keeping data flowing in and modeling business logic correctly are two separate problems. Companies used to force both onto one person.
What is data lineage and why does it matter?
Lineage is the recorded path data takes. It goes from the source system, through each transformation, to the dashboard or model that uses it. It matters for three everyday reasons. When a number looks wrong, lineage shows where it came from. When a source schema changes, lineage shows what will break. When a privacy request comes in, lineage shows where personal data ended up. Teams without it spend a lot of their week asking "where does this number come from?"
What qualifications do you need to become a data engineer?
Few data engineers have a data engineering degree. Most come from backend development, database administration, or analytics. A computer science or math background helps but is not required. Cloud and warehouse certifications are fairly well respected here. What employers really test is SQL depth and pipeline design judgment. Both are easy to test directly and hard to fake.
What interview questions should I ask a data engineer?
Give them a real ingestion problem. For example, daily loads from an API that sometimes sends duplicates and sometimes changes schema. Ask how they would design it. Listen for idempotency and how they handle schema changes. Ask how they would model a messy source so analysts can use it. Show a slow query and ask what they would check. Ask about a pipeline failure that produced wrong numbers nobody noticed for a week. What did they change after? Skip trivia about specific tools.
How do you screen data engineers efficiently?
Start with a short SQL test on joins, window functions, and deduplication. It filters a large applicant pool fast and tracks well with real job skill. Then hold a structured pipeline design conversation. Score every candidate on the same rubric. SQL skill alone does not tell you if someone designs jobs that recover from failure. That is the costly part to get wrong.

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