Senior Data Engineer

Colorado Rockies•Denver, CO

18h•$140,000 - $165,000•Hybrid

About The Position

The Colorado Rockies Baseball Club is building an integrated baseball information platform that brings together scouting reports, player statistics, rosters, performance data, and other Baseball Operations information to support timely and informed decision-making. The Senior Data Engineer will design, build, maintain, and optimize the pipelines, data models, orchestration, and cloud infrastructure that power analytics and internal applications across Baseball Operations. This role will partner closely with Baseball Systems, analysts, developers, and end users to ensure data is accurate, accessible, reliable, and scalable. In addition to hands-on engineering responsibilities, the Senior Data Engineer will provide technical leadership by establishing best practices, improving system reliability, and mentoring other engineers.

Requirements

Five or more years of data engineering experience, or an equivalent combination of experience and demonstrated capability.
Significant experience building and operating reliable production data pipelines (ETL/ELT).
Advanced SQL and strong proficiency in Python.
Hands-on experience with a major cloud platform (AWS, Azure, or GCP) and a modern data platform (Databricks, Snowflake, or equivalent).
Experience developing distributed data workloads with Apache Spark / PySpark.
Experience across data modeling, orchestration, testing, observability, and production troubleshooting.
Ability to guide technical decisions and mentor less experienced engineers.
Strong communication and collaboration with both technical and non-technical stakeholders.

Nice To Haves

Experience with baseball data and Baseball Operations workflows — tracking, scouting, performance, player development, sports science, or biomechanics.
Experience with lakehouse, data warehouse, or data lake architecture, including scalable ingestion patterns and dimensional or layered data modeling.
Experience with workflow orchestration (Apache Airflow, Dagster, Prefect, or similar) and pipeline monitoring, data-quality testing, and operational support.
Experience building data pipelines, feature datasets, or platform infrastructure for machine learning and artificial intelligence workloads.
Familiarity with infrastructure as code (Terraform, AWS CDK, or CloudFormation).
Bachelor's degree in a related technical field, completion of an immersive technical program, or equivalent practical experience.
Familiarity with AI-assisted development tools and their responsible use in modern engineering workflows.

Responsibilities

Design, build, and maintain scalable ETL and ELT pipelines that ingest data from external vendors, internal systems, and baseball data sources.
Develop and maintain cloud-based data infrastructure supporting storage, transformation, orchestration, analytics, and internal applications.
Model, structure, and optimize data for use by analysts, developers, researchers, and other Baseball Operations users.
Monitor and troubleshoot production pipelines, identify root causes, resolve data quality issues, and improve reliability and performance.
Partner with developers, analysts, and Baseball Operations stakeholders to translate business needs into scalable data solutions.
Establish and promote engineering best practices related to code quality, documentation, testing, version control, observability, and maintainability.
Evaluate, integrate, and manage new baseball data sources, vendor feeds, APIs, and file-based deliveries.
Support data infrastructure used for analytics, machine learning, artificial intelligence, and internal decision-support tools.
Mentor other engineers through technical guidance, code reviews, project support, and skill development.