3+ years in data engineering with a focus on data warehousing — and the appetite to take on more ownership than you've held so far
Strong SQL and Python for data work
Working with AI tools feels natural to you — and, just as important, the judgment to review and validate what they produce. You treat AI output as a draft to verify, not an answer to trust, especially where correctness is non-negotiable
A solid grasp of data architecture and modeling principles (dimensional modeling, slowly-changing dimensions, incremental patterns) — or the drive to deepen it fast
Hands-on experience with dbt on a cloud warehouse — this is where you'll live day to day
Working knowledge of relational and some exposure to non-relational Databases (DynamoDB, MongoDB)
A track record of debugging tricky data issues and shipping durable fixes
Excellent written and verbal English
AWS experience (Redshift, EMR, Glue, S3, IAM); experience with GCP or Azure is also welcome
Streaming/CDC technologies (Kafka, Kinesis, Debezium) or data mesh
Experience building data pipelines for ML or AI systems
Exposure to financial, payments, or regulated reporting data
German (helpful for some stakeholder work, but not required)