– Strong SQL. You’re comfortable with complex queries — CTEs, window functions, recursive
logic, set-based thinking — and you can read a query someone else wrote and work out both
what it does and why it’s slow. Experience with stored procedures (PL/pgSQL, PL/SQL, T-SQL
or similar) is a significant plus.
– Solid Python. Practical, production Python: clear code, tests, type hints, packaging and
libraries.
– Strong analytical thinking. The ability to take a vague symptom, form a hypothesis, test it
against evidence, and arrive at a cause rather than a workaround.
– 3+ years of professional back-end or data engineering experience, including owning
features from design through to production.
– Experience working in a Scrum team — refinement, estimation, code review, and
communicating clearly about progress and blockers, across a distributed team.
– Git and pull-request-based development.
– Fluent communication in English.

Valuable, but you can learn it here:
– Data Vault 2.0, or another data warehousing architecture (Kimball, Inmon), and genuine
interest in the modelling problems.
– Experience with any of our target platforms: Snowflake, Databricks, BigQuery, Azure Synapse,
Microsoft Fabric, PostgreSQL, Oracle, SQL Server, Greenplum, AWS Redshift, SingleStore or
Apache Spark.
– ETL and orchestration tooling: dbt, Apache Airflow or Azure Data Factory.
– Code generation, template engines, parsers, or metadata-driven systems in general.
– JVM experience — some of our code runs on the JVM.
– Experience using AI coding tools such as Claude or Cursor as part of your daily workflow.