Note: The job is a remote job and is open to candidates in USA. Teamworks is the leading sports tech platform, powering over 6,500 organizations worldwide. They are seeking a Staff Data Engineer to define the technical direction of their data platform, establish standards, and make architectural decisions that enhance data analytics, ML, and AI capabilities for athletic performance. The role involves hands-on development and strategic leadership in building a modern lakehouse for data integration and analytics.

Responsibilities

• 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

Skills

• 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
• 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
• You have integrated legacy or acquired products into a lakehouse architecture
• You bring software engineering depth beyond data engineering, in platform-as-a-product environments where internal teams are the customers
• You're 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

Benefits

• Offers Equity
• Offers Bonus
• FullTime
• Remote

Company Overview

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