Hire Data Engineers who can actually ship

A data engineer builds and runs the systems that move data from where it is created to where it is used. They pull data from apps, databases, files and third-party tools. They clean and shape it, and they load it into a warehouse or lake where analysts, analytics engineers and data scientists can trust it.

In practice, a data engineer owns three things:

• Ingestion. Getting data in reliably, on schedule, without silent gaps.
• Transformation. Turning raw data into tables with clear grain, keys and definitions.
• Serving. Making data available to dashboards, models and products at the speed and cost the business needs.

Good data engineers are accountable for data that actually lands where it is needed. They don't just close tickets.

You probably need a data engineer when:

• Your analysts spend more time fixing broken exports than answering questions.
• Reports disagree because every team pulls data its own way.
• You are moving to a cloud warehouse or lakehouse and need someone to own the migration.
• A product feature, model or customer report depends on data arriving on time, every time.
• Your first data hire was an analyst, and the pipelines around them are now held together with scripts.

If you need someone to model trusted tables for reporting on top of an existing platform, an Analytics Engineer may be the better first hire. If you need answers and dashboards, look at a Data Analyst.

Owns ingestion → transform → serving. Strong data engineers can explain where every important table comes from, how often it refreshes and what happens when a source fails. They build for the Tuesday when an API changes its schema, not only for the demo.

SQL and Python that survive production. SQL is the daily tool. Python (or Scala/Java where the stack needs it) handles ingestion, orchestration and anything SQL can't. Look for code that another engineer can read, test and rerun safely. Idempotent loads, sensible retries and backfills that don't double-count are all good signs.

Cloud pragmatism: Azure, AWS or GCP. They choose the right service for the job and can explain the cost. No vendor religion. They know when a managed service is cheaper than building, and when it isn't.

Data modelling and quality. They think about grain, keys and slowly changing data. They add tests and alerts so problems surface before a stakeholder finds them.

Communicates trade-offs. They work with analytics, platform and product partners, and explain cost, latency and risk in plain language.

What to probe in interviews

• "Walk me through a pipeline you owned that broke. What happened and what did you change?"
• "How would you backfill six months of data without breaking today's reports?"
• "When would you choose batch over streaming for this use case?"

Warning signs

• A CV that lists every tool but can't describe a single failure they fixed.
• Only notebook or demo pipelines, with nothing that ran in production.
• A ticket-taker mindset: "I built what was asked", with no view on whether it was used.

These are examples, not requirements. We match the stack in your brief.

• Languages: SQL, Python; Scala or Java for Spark-heavy stacks.
• Warehouses and lakes: Snowflake, BigQuery, Redshift, Databricks, Azure Synapse / Fabric, Postgres.
• Orchestration: Airflow, Dagster, Prefect, Azure Data Factory.
• Transformation: dbt, Spark, SQL-based ELT.
• Ingestion: Fivetran, Airbyte, custom API connectors, CDC.
• Streaming (when the role needs it): Kafka, Kinesis, Pub/Sub, Event Hubs.
• Platform: Docker, Terraform or other infrastructure-as-code (a plus), CI/CD, Git.

There are two ways to use carbon.talent. Both use the same profile.

Option 1: Post a role (free)

1. Create your role. Title, stack, seniority, engagement type (permanent, contract or fractional), location and overlap hours. Posting is free.
2. Match against Carbon’s talent. When you're ready, you compare the role with data engineers who have already been assessed on carbon.talent.
3. Review the profile. Vetted profiles come with a vetting report, a short video intro and a working-style profile. You also get an interview guide tailored to your role and the candidate.
4. Interview and hire. Interview fewer, better people. Agree the rate, draft the contract and sign on the platform.

Option 2: Test a candidate you found Already found someone on LinkedIn, through your own network? Before you make an offer, send a SQL or Python skills test or the real-world data experience test, then review the report. The real-world data experience test is on every plan; SQL and Python tests are on paid plans. Add a working-style profile and a short video.

A CV is self-reported. Job titles mean different things at different companies, and remote hiring makes it harder to be sure who you are talking to. When a hiring process starts from CVs, your team does the checking in interviews, one expensive hour at a time.

carbon.talent profiles carry scenario-based vetting, a working-style profile and a video intro.

One flat price per company. The real-world data experience test, working-style profile and video are free on every plan. Connect your AI assistant (MCP) on Team and Scale. SQL and Python skills tests are on paid plans. Webhooks and ID checks are on Team and Scale.

carbon.talent is a global hiring platform. Companies post data engineering roles from anywhere, and you choose where your hire is based and which hours they overlap with your team.

Hire where your work happens. Set the country, time zone overlap and currency on each role, and review people who fit.

• Engagement. Hire permanent, on contract or fractional through the same platform.

Hiring locally? The same process works for on-site and hybrid roles in any city. Location is a setting on your role, not a separate product.

Data engineer salaries vary widely by country, seniority, stack and sector. Platform and streaming skills, cloud certifications and ownership of a migration usually move pay up. Benchmark against your own market before you set a budget. Salary is agreed between you and the professional, and it is separate from the carbon.talent platform.