Staff Data Engineer

Teamworks

16d•$216,000 - $216,000•Hybrid

About The Position

The Senior Manager of Engineering is seeking a Staff Data Engineer to join their Data Platform team. This role will be instrumental in building the foundation for integrating athlete performance data, product telemetry, and acquired datasets from the sports tech space. The team is constructing a modern lakehouse to serve as the backbone for cross-product analytics, machine learning, and AI features. This position offers a strategic and hands-on opportunity to co-define the technical direction, establish engineering standards, and make critical architectural decisions. The work is highly visible, organizationally supported, and directly impacts on-field capabilities for athletes and coaches.

Requirements

10+ years of data engineering or related experience.
Strong Python skills for pipelines, transformations, and platform tooling.
Deep expertise in designing, operating, and setting direction for lakehouse platforms (Delta Lake, Iceberg, or Hudi) at production scale.
Deep expertise with modern processing engines (Spark, Databricks, Trino, or Snowflake) at production scale.
Expert AWS and distributed cloud architecture experience (S3, IAM, Glue, EMR/Lambda, networking).
Fluent in writing Terraform and implementing best practices for cloud designs.
Deep data modeling and schema design for complex entities (time-series, hierarchical, multi-source) in multi-tenant environments.
Proven experience building warehouses, lakehouses, and relational systems.
Proven integration standards across teams (event-driven, API, batch).
Track record of establishing or significantly maturing a data platform from ambiguous goals.
Experience aligning leaders and teams and communicating decisions to senior and non-technical stakeholders through RFCs and ADRs.
Familiarity with introducing data governance, ownership, and stewardship programs.

Nice To Haves

Sports industry experience.
Experience using a lakehouse to ingest multi-source performance data (Catapult, Vald, Kinexon) and model it for products, analytics, and ML.
Experience integrating legacy or acquired products into a lakehouse architecture.
Software engineering depth beyond data engineering, particularly in platform-as-a-product environments.
AI-forward experience with tools like Claude or Cursor.
Experience with the data foundation (catalog, semantic layer) that enables AI agents to reason over data.

Responsibilities

Define the technical architecture and platform standards for the lakehouse on AWS, including distributed cloud architecture, schema conventions, multi-tenant isolation, and integration design.
Lead the design and delivery of production pipelines for consolidating performance and product data.
Own data modeling for complex entities (time-series, hierarchical, multi-source) to support products, analytics, and ML.
Introduce data governance, ownership, and stewardship to enhance data maturity.
Lay the foundation for a catalog and semantic layer that analytics, ML, and AI agents can utilize.
Author and maintain the Data Platform playbook, including reusable patterns, ADRs, runbooks, and Terraform modules, with a focus on data quality and reliability for self-service by product teams.
Lead end-to-end delivery, from requirements and planning to coordinating workstreams and communicating status to senior leadership and non-technical partners.
Mentor engineers across different levels.
Raise the engineering bar through design reviews and on-call ownership.
Act as the engineering voice in shaping the platform roadmap.