How we interview · Data engineer

Hire data engineers who build pipelines you can trust

Data and analytics engineers, mid-level to staff, interviewed on data modelling, pipeline design, data quality and cost, the things that decide whether anyone trusts your numbers.

When data engineering goes wrong, nobody sees an error. They see a dashboard that's slightly off, a report two teams disagree on, or a warehouse bill that doubled. Good data engineers prevent all three.

We look for engineers who model data so it means the same thing everywhere, build pipelines that fail loudly instead of silently, test the data as well as the code, and keep an eye on what each query costs.

What they work on: SQL, Python, Spark, Airflow or Dagster, dbt, Kafka, and warehouses such as BigQuery, Snowflake and Redshift.

When to hire one

Teams argue about whose numbers are right.
Pipelines break quietly and someone finds out a week later.
Your warehouse costs keep climbing.

What we assess in data engineers

Every report also covers impact, judgement, how they raise the team and how they work with others.

Craft

Data modelling: Tables that match how the business thinks, with clear grain and definitions.
Pipelines: Batch and streaming, orchestration, backfills and idempotent reruns.
Data quality: Tests, contracts and alerts that catch bad data before it reaches a dashboard.
Performance and cost: Partitioning, incremental loads and knowing what a query costs.
Working with consumers: Understanding what analysts, ML and product teams actually need.

How we recommend a level

Mid-level: Builds and maintains reliable pipelines and models within an existing design.
Senior: Designs the data model and pipelines for an area, with monitoring and clear ownership.
Staff / lead: Sets data architecture and standards across teams and makes the cost and quality trade-offs.

The 60-minute interview

Warm-up (10 min): A pipeline they built: what fed it, who used it, and the worst day it had.
Modelling (20 min): Model a real domain, such as subscriptions or orders, for reporting.
Hands-on (25 min): Fix and harden a broken pipeline: late data, duplicates and a needed backfill.
Their questions (5 min): Do they ask who uses the data and how fresh it must be?

Red flags we screen for

Can't explain the grain of a table they built.
No tests on data, only on code.
Never looked at what their queries cost.

What you receive for each shortlisted candidate:

Candidate T.R.

Senior Data Engineer · 60-minute technical interview

Sample report

Tested: Modelling subscriptions for revenue reporting, and fixing a pipeline with late and duplicate events.
Strong: Clear grain and definitions. Made reruns idempotent. Added data tests without prompting.
Gaps: Little streaming experience. Didn't consider warehouse cost until asked.
Level: Senior.
Probe next: A cost problem they solved, and how they'd move this pipeline to near real time.

RecommendationShortlist

Questions about hiring data engineers

Yes. If the role is mostly modelling and dbt for reporting, we interview for that; if it's pipelines and infrastructure, we weight those instead. We agree the balance at the brief.

First interviewed profiles usually reach you within a few days of the brief, and selection through your own rounds takes about two weeks. Joining then depends on the candidate's notice period, which in India is often 30 to 90 days.

A percentage of the hire's annual CTC, set by seniority and paid only when the candidate joins. There's no retainer and no fee if nobody joins.

Some can, and we'll tell you in the report. If analysis is a big part of the job, say so at the brief so we test for it.

Other engineering roles we hire

Hiring on your own terms? Permanent hiring, contract hires or interviews only.

Tell us the role.

Four details and we'll come back within a working day with how we'd approach it.

No retainer. You pay only when someone joins. Or write to talent@algosmiths.com