Hire Analytics Engineers who make data trustworthy

An analytics engineer sits between data engineering and analysis. Data engineers get raw data into the warehouse. Analysts and business teams ask questions of it. The analytics engineer turns raw tables into clean, tested, documented models that everyone can rely on.

In practice, an analytics engineer:

• Builds and maintains the modelling layer, often in dbt, as staging, intermediate and mart models.
• Defines business logic once, such as "active customer", "net revenue" or "churn", so every dashboard uses the same answer.
• Adds tests, freshness checks and documentation, so problems show up before a stakeholder finds them.
• Brings software practice to analytics work: version control, code review, CI and clear naming.

You probably need an analytics engineer when:

• Three teams calculate the same metric three different ways.
• Analysts spend their week rewriting the same joins instead of answering questions.
• Your dbt project has grown without tests, docs or a clear structure.
• Leadership has stopped trusting the dashboards.
• You have raw data in a warehouse, but nobody owns the layer between it and reporting.

If data isn't reliably landing in your warehouse yet, hire a Data Engineer first. If your models are solid and you need answers and insight, look at a Data Analyst.

Modelling craft: grain, keys and definitions. Strong analytics engineers can state the grain of every model they build. They choose keys deliberately, handle slowly changing dimensions and write business definitions down. Dimensional or mart thinking comes naturally.

Strong SQL and dbt (or equivalent). Warehouse SQL is the core skill. They know dbt well, including refs, sources, macros, incremental models and packages, or an equivalent tool your team uses. More importantly, they know when not to add complexity.

Testing and documentation as habit. Uniqueness and not-null tests on keys, relationship tests, freshness checks and documented columns. Trust comes from failures that are visible and fixed quickly, not from hoping nothing breaks.

Modern ELT without vendor religion. They work comfortably across Snowflake, BigQuery, Databricks or Redshift, and pick the approach that fits your stack and budget.

Partners with Data Engineering and BI. They agree contracts with upstream engineers, and they make sure analysts and business users understand what each model means. They don't work in a silo.

What to probe in interviews

• "Pick a model you built. What is its grain, and how do you know it's correct?"
• "Two dashboards show different revenue numbers. How would you find out why?"
• "How do you decide what gets a test, and what happens when a test fails at 6am?"

Warning signs

• "dbt" on the CV, but no testing culture and no view on project structure.
• A "dashboard tourist": lots of charts, no ownership of the models underneath.
• Untested marts that only the author understands.

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

• Transformation: dbt Core or dbt Cloud, SQLMesh, or warehouse-native SQL.
• Warehouses: Snowflake, BigQuery, Databricks, Redshift, Postgres, Microsoft Fabric.
• Testing and quality: dbt tests, dbt-expectations, Great Expectations, Elementary.
• Semantic layer and metrics (when the role asks): dbt Semantic Layer, LookML, Cube.
• BI: Looker, Power BI, Tableau, Metabase.
• Workflow: Git, pull requests, CI, orchestration with Airflow or Dagster.

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 analytics 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 an analytics engineer 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 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.

Analytics engineer salaries vary by country, seniority, stack and sector. People who own a whole modelling layer, its standards and its tests usually earn more than those who maintain individual models. 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.