Data Platform Engineer

REQUIREMENTS

• 5+ years of data engineering experience, including real ownership of a production platform.
• Python to a production standard.
• Advanced SQL, including window functions, CTEs, and query performance.
• Experience with dbt, including models, snapshots, incremental materialisations, and testing.
• Deep AWS expertise, including ECS, VPC networking (subnets, security groups, NAT, VPC endpoints), IAM, Secrets Manager, and S3. You should have built and managed these yourself rather than simply using infrastructure provided by a platform team.
• Experience with Docker, Docker Compose, Git, and CI pipelines.
• Solid understanding of layered data warehouse design, incremental loading, idempotency, and slowly changing dimensions.
• Highly autonomous, with the ability to set priorities independently, make architectural decisions, document systems proactively, and communicate technical constraints clearly to non-technical stakeholders.

NICE TO HAVE

Experience with any of the following is advantageous:

• Apache Iceberg
• Trino
• PyIceberg / PyArrow
• Apache Nessie
• Power BI
• Salesforce data model
• BigQuery
• Google Ads data model
• Experience owning a Snowflake, Databricks, or BigQuery data warehouse will be considered equivalent. An exact technology stack match is not expected.

CURRENT STATE OF WAREHOUSE

You will inherit a well-structured and thoroughly documented data warehouse, including:

• A complete architecture and operations guide.
• Consistent engineering conventions.
• Configuration-driven data ingestion.
• Working failure alerting.
• A backlog containing most important next steps.
• Thorough documentation of sources consumed into the warehouse.

REPORTING LINE

• Reports to the CTO.

WORKING ARRANGEMENT

• Fully remote.