Data Engineer

Premier Lacrosse League•El Segundo, CA

4d•$80,000 - $95,000•Onsite

About The Position

The Premier Lacrosse League is seeking a Data Engineer who is passionate about turning raw data into actionable insights that drive business decisions and elevate the sport of lacrosse. You will own our business intelligence infrastructure and lacrosse statistics pipelines, partnering with Marketing, Product, and Engineering teams to measure campaign effectiveness, analyze product features, and surface insights that inform strategy. This role sits at the intersection of sports analytics and business intelligence, giving you the opportunity to shape how data is collected, modeled, and consumed across the organization.

Requirements

3+ years of experience in data engineering, analytics engineering, or a related role
Expert-level SQL skills with hands-on experience querying and optimizing BigQuery (or comparable cloud data warehouses like Snowflake or Redshift)
Production experience with Looker or similar BI platforms (Tableau, Mode, Metabase)
Demonstrated fluency with AI coding assistants (Cursor, Claude Code, Codex) as part of your daily workflow
Python experience for data transformation, scripting, and pipeline development (preferred)
Experience building and maintaining ETL/ELT pipelines using tools like Fivetran, dbt, Airflow, or similar
Strong communication skills with the ability to translate technical findings into business insights for non-technical stakeholders
Curiosity about sports, fan behavior, and the unique data challenges in live entertainment

Nice To Haves

Experience with sports statistics or media/entertainment data is a plus

Responsibilities

Create and maintain dashboards and reports in Looker (or similar BI platforms) that enable Marketing, Product, and leadership to make data-driven decisions.
Design, build, and maintain robust ETL/ELT pipelines that power business intelligence dashboards, lacrosse statistics platforms, and cross-functional reporting needs.
Write complex, performant SQL queries against BigQuery to answer ad-hoc business questions, build data models, and support self-serve analytics across teams.
Collaborate with Marketing to measure campaign performance, with Product to analyze feature adoption and user behavior, and with Engineering to ensure data quality and accessibility.
Build and maintain data pipelines for player and team statistics, supporting broadcast integrations, fantasy products, and fan-facing experiences.
Leverage AI coding tools like Cursor or Claude Code to accelerate development, automate repetitive tasks, and prototype solutions rapidly.
Design clean, scalable data models that balance performance with usability for downstream consumers.