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I'm Scott Roberts https://www.linkedin.com/in/scottrobertsprofile/, 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 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 I'M LOOKING FOR 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 produc