AI-First Data Engineer (m/f/d)
$288,000–$336,000 year
Remote
Job Summary
Build and optimize cloud-native data pipelines on AWS using ETL/ELT, dbt, Spark, and Airflow while integrating AI tooling as a standard workflow component. Design and evolve data models that power analytics, operational use cases, and AI/ML systems across thousands of studios, ensuring correctness and auditability for financial reporting. Embed governance, testing, and lineage to raise the bar on data reliability, then partner with product teams to transform ambiguous business problems into robust data solutions. Evaluate emerging approaches like data mesh patterns and new AI tools to determine their fit for the stack. Debug tricky data issues and ship durable fixes while maintaining strict validation of AI outputs.
Required Qualifications
- 3+ years in data engineering with a focus on data warehousing
- 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)
- Hands-on experience with dbt on a cloud warehouse
- 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
- Build and optimize cloud-native data pipelines on AWS — ETL/ELT and the infrastructure underneath (Aurora, Redshift, dbt, Spark/EMR, Airflow, CDC)
Desired Qualifications
- AWS experience (Redshift, EMR, Glue, S3, IAM)
- experience with GCP or Azure
- 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
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