Build and optimize cloud-native data pipelines on AWS — ETL/ELT and the infrastructure underneath (Aurora, Redshift, dbt, Spark/EMR, Airflow, CDC) — using AI tooling as a standard part of the workflow.
Design and evolve the data models that power analytics, operational use cases, and AI/ML across thousands of studios.
Help raise the bar on data reliability — embed governance, testing, and lineage so both people and AI systems can trust the numbers by default.
Partner with product, and other engineering teams to turn ambiguous business problems into robust data solutions.
Evaluate and evolve new approaches with us — data mesh patterns, and emerging AI tooling — and help decide what genuinely earns a place in our stack.
Grow into our financial and regulatory reporting workstream (Finion Capital), where correctness and auditability matter most.

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.

Build and optimize cloud-native data pipelines on AWS — ETL/ELT and the infrastructure underneath (Aurora, Redshift, dbt, Spark/EMR, Airflow, CDC) — using AI tooling as a standard part of the workflow.
Design and evolve the data models that power analytics, operational use cases, and AI/ML across thousands of studios.
Help raise the bar on data reliability — embed governance, testing, and lineage so both people and AI systems can trust the numbers by default.
Partner with product, and other engineering teams to turn ambiguous business problems into robust data solutions.
Evaluate and evolve new approaches with us — data mesh patterns, and emerging AI tooling — and help decide what genuinely earns a place in our stack.
Grow into our financial and regulatory reporting workstream (Finion Capital), where correctness and auditability matter most.

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.

AI-First Data Engineer (m/f/d)

Skills