About the role
I'm Scott Roberts , Senior Manager, Engineering at Teamworks. I lead the Data Platform team, and we're building the foundation that brings together athlete performance data, product telemetry, and the unique datasets we've accumulated through several acquisitions in the sports tech space. Right now, a lot of that data lives in disparate systems and original tech stacks, and much of it isn't yet defined or organized well enough for us to fully leverage it for analytics, ML, and the AI features we're building. My team is changing that by building a modern lakehouse that becomes the backbone of our cross-product analytics, ML, and AI, with just enough structure and ownership to move us up a level in data maturity. This is where you come in. I'm looking for a Staff Data Engineer who can co-define the technical direction of this platform, establish the standards other engineers build on, and make architectural decisions that will matter for years. You will be strategic and hands-on, as comfortable shaping the roadmap and bringing other leaders and teams along as you are writing the Python and building the pipelines. The work is highly visible, organizationally backed, and tied directly to capabilities that show up on the field for athletes and coaches.
The 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
