LA Galaxy, Data Engineer - Soccer Operations

AEG Worldwide•Carson, CA

About The Position

The LA Galaxy Data Engineer position is responsible for designing, developing, and maintaining robust data pipelines that transform raw information into actionable intelligence for analysts, coaches, scouts, and decision-makers. The Data Engineer will integrate and manage data streams from external providers, ensure high-quality and reliable data flows, and partner closely with the Soccer Operations Data team to support advanced analytics, machine learning, and predictive modeling. This role will also build self-service tools and platforms that empower nontechnical users across player recruitment, performance analysis, tactical evaluation, and opposition scouting. The work produced in this role will directly influence how the club identifies talent, prepares for matches, and competes on the field.

Requirements

Design, implement, and own a real-time pipeline: Design, implement, and maintain low-latency pipelines that ingest, process, and deliver high-volume event and tracking data in real time. Own pipeline reliability, monitoring, and performance optimization to ensure uninterrupted data availability for analysts, applications, and models. Support both live match analysis and post-match workflows with minimal delay.
Create self-service tools: Develop intuitive dashboards, query interfaces, and internal applications that enable nontechnical users to explore and extract insights independently. Reduce reliance on ad hoc engineering requests by building scalable, well-documented tools that balance flexibility with governance and data integrity.
Partner with data scientists: Partner with data scientists to transition machine learning models from experimentation to production. Build deployment pipelines, monitoring systems, and retraining workflows to ensure models operate reliably at scale. Support use cases such as recruitment analysis, player valuation, and contract decision-making.
Collaborate with video analysts: Collaborate with video analysts to align positional tracking data with match footage for frame-accurate analysis. Develop workflows and tooling that connect quantitative data with visual context to support tactical reviews, player evaluations, and coaching presentations.
Participate in regular meetings with the Pro Analytics, Soccer Operations, and Performance teams to align on priorities and ensure data solutions meet evolving needs. Communicate technical concepts clearly to nontechnical stakeholders and contribute to long-term data strategy and standards.

Responsibilities

Design, implement, and own a real-time pipeline: Design, implement, and maintain low-latency pipelines that ingest, process, and deliver high-volume event and tracking data in real time. Own pipeline reliability, monitoring, and performance optimization to ensure uninterrupted data availability for analysts, applications, and models. Support both live match analysis and post-match workflows with minimal delay.
Create self-service tools: Develop intuitive dashboards, query interfaces, and internal applications that enable nontechnical users to explore and extract insights independently. Reduce reliance on ad hoc engineering requests by building scalable, well-documented tools that balance flexibility with governance and data integrity.
Partner with data scientists: Partner with data scientists to transition machine learning models from experimentation to production. Build deployment pipelines, monitoring systems, and retraining workflows to ensure models operate reliably at scale. Support use cases such as recruitment analysis, player valuation, and contract decision-making.
Collaborate with video analysts: Collaborate with video analysts to align positional tracking data with match footage for frame-accurate analysis. Develop workflows and tooling that connect quantitative data with visual context to support tactical reviews, player evaluations, and coaching presentations.
Participate in regular meetings with the Pro Analytics, Soccer Operations, and Performance teams to align on priorities and ensure data solutions meet evolving needs. Communicate technical concepts clearly to nontechnical stakeholders and contribute to long-term data strategy and standards.