Responsibilities
- Establish technical architecture and platform standards for the AWS-based lakehouse, covering distributed cloud architecture, schema conventions, multi-tenant isolation, and integration design
- Oversee design and implementation of production pipelines for consolidating performance and product data, and manage data modeling for complex entities to support products, analytics, and machine learning
- Implement appropriate data governance, ownership, and stewardship to enhance data maturity, and build catalog and semantic-layer foundations for analytics, ML, and AI agents
- Create and maintain the Data Platform playbook with reusable patterns, ADRs, runbooks, and Terraform modules, ensuring 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 at all levels, improve standards through design reviews and on-call ownership, and influence the platform roadmap as an engineering representative
Requirements
- 10 or more years in data engineering or related roles, with advanced Python skills for pipelines, transformations, and platform tooling
- Expertise in designing, operating, and guiding lakehouse platforms and modern processing engines at production scale, with strong decision-making and troubleshooting abilities
- Advanced AWS and distributed cloud architecture experience, including S3, IAM, Glue, EMR/Lambda, and networking, with proficiency in Terraform and implementation best practices
- Deep knowledge of data modeling and schema design for complex entities in multi-tenant environments, across various systems, and proven integration standards across teams
- Experience in developing or significantly improving a data platform from vague objectives, including organizational alignment and communication with stakeholders through RFCs and ADRs
- Understanding of data governance, ownership, and stewardship programs, with the judgment to apply them appropriately to increase data maturity without unnecessary complexity
Nice to Have
- Sports industry experience with lakehouse ingestion of multi-source performance data and modeling for products, analytics, and ML
- Experience integrating legacy or acquired products into a lakehouse architecture
- Software engineering expertise beyond data engineering, in platform-as-a-product environments with internal teams as customers
- Familiarity with AI tools and a perspective on data foundations that enable AI agents to reason over data
Other
- This role is not eligible for sponsorship at this time.
- The company is an equal opportunity employer valuing honesty, hard work, humility, commitment, innovation, and exceptional character.
- Commitment to building a diverse and inclusive workforce without discrimination based on protected characteristics.
- Non-discrimination policy applies to all employment practices including recruiting, hiring, promotion, termination, compensation, benefits, and training.
- Reasonable accommodations available for candidates with disabilities during the hiring process; contact talent@teamworks.com to request.
This role is not eligible for sponsorship at this time.
