Staff Data Engineer
Role
Define the technical architecture and platform standards for our lakehouse on AWS: distributed cloud architecture, schema conventions, multi-tenant isolation, and integration design
Lead design and delivery of the production pipelines that consolidate performance and product data, and own data modeling for complex entities (time-series, hierarchical, multi-source) so the models serve products, analytics, and ML
Introduce just enough data governance, ownership, and stewardship to raise our data maturity, and lay the catalog and semantic-layer foundation that analytics, ML, and AI agents can reason over
Author and maintain the Data Platform playbook (reusable patterns, ADRs, runbooks, Terraform modules) with data quality and reliability built in, so product teams can self-serve new datasets and integrations
Lead delivery end to end, from requirements and planning through coordinating workstreams and translating status to senior leadership and non-technical partners
Mentor engineers across levels, raise the bar through design review and on-call ownership, and be the engineering voice shaping the platform roadmap
What You Must Bring
- 10+ years of data engineering or related experience, with strong Python for pipelines, transformations, and platform tooling
- Deep expertise designing, operating, and setting direction for lakehouse platforms (Delta Lake, Iceberg, or Hudi) and modern processing engines (Spark, Databricks, Trino, or Snowflake) at production scale, with the judgment to make the hard tradeoffs and troubleshoot them
- Expert AWS and distributed cloud architecture experience (S3, IAM, Glue, EMR/Lambda, networking), fluent writing Terraform and the best practices for implementing those designs
- Deep data modeling and schema design for complex entities (time-series, hierarchical, multi-source) in multi-tenant environments, across multiple systems you've built (warehouses, lakehouses, relational), plus proven integration standards across teams (event-driven, API, batch)
- Track record of standing up or significantly maturing a data platform from ambiguous goals, including the organizational work of aligning leaders and teams and communicating decisions to senior and non-technical stakeholders through RFCs and ADRs
- Familiarity with how data governance, ownership, and stewardship programs are introduced, and the judgment to apply just enough to raise data maturity without over-engineering it
Even Better If You Have
- Sports industry experience and have used a lakehouse to ingest multi-source performance data (Catapult, Vald, Kinexon) and model it for products, analytics, and ML
- Integrated legacy or acquired products into a lakehouse architecture
- Bring software engineering depth beyond data engineering, in platform-as-a-product environments where internal teams are the customers
- Are AI-forward with tools like Claude or Cursor, and have a point of view on the data foundation (catalog, semantic layer) that lets AI agents reason over our data
Why This Role
The platform you help define ties our data together across products, gives it the structure to fuel analytics, ML, and AI agents, and puts us in a position no one else in the industry has. If you want to set technical direction with real organizational backing and see your architectural decisions translate directly to outcomes on the field, this is that role.
About Teamworks
We're the Operating System for Sports™, powering 7,000+ organizations worldwide, from collegiate programs to every major pro league. Founded in 2006, we've evolved from a messaging tool for college football into the leading sports tech platform, with 600+ global teammates building the future of sports tech. Our solutions span Personnel, Coaching, Performance, Operations, and Intelligence - helping teams recruit smarter, train better, stay compliant, and win.