As a Data Engineer, you will own key parts of the pipeline lifecycle—from ingesting source data through transformation, testing, and publishing trusted datasets for downstream consumers. You’ll partner closely with analysts and stakeholders to turn questions into durable data products, improve reliability and observability, and help standardize patterns that scale across teams. Success in this role looks like dependable pipelines, well-modeled data, and faster delivery of insights.
Responsibilities:
- Design, develop, and maintain robust, scalable data pipelines and ETL/ELT workflows to support analytics, reporting, and machine learning initiatives
- Build and optimize data models (dimensional, relational) across structured and semi-structured data sources including ticketing, fan engagement, broadcasting, and sponsorship data
- Develop and maintain production-grade Python applications and scripts for data transformation, API integrations, and automation
- Engineer solutions on Databricks or Snowflake for large-scale data processing, lakehouse architecture, and advanced analytics
- Build and deploy serverless data solutions using Azure Functions for event-driven processing and microservice integrations
- Design and implement data orchestration workflows using platforms such as Apache Airflow and/or Astronomer to ensure reliable, monitored, and scalable pipeline execution
- Manage version control, CI/CD pipelines, and collaborative development workflows using Git-based platforms (GitHub, Azure DevOps)
- Collaborate with data analysts, data scientists, and business stakeholders to translate requirements into technical solutions
- Implement data quality frameworks, monitoring, and alerting to ensure data integrity and reliability across the platform
- Contribute to the evolution of the data platform architecture, advocating for best practices in performance, security, and scalability
- Participate in code reviews to uphold engineering standards
Qualifications:
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field (or equivalent professional experience)
- 3+ years of professional experience in data engineering or a related discipline
- Strong relational database experience, including data modeling (star schema, snowflake schema, 3NF) and advanced SQL development (T-SQL, PL/SQL, or equivalent)
- Proficiency in Python development for data engineering use cases (pandas, PySpark, API development, scripting, testing)
- Hands-on experience with Databricks or Snowflake for data lakehouse/warehouse architecture and large-scale data processing
- Experience building and deploying Azure Functions or similar serverless compute for data workflows
- Working knowledge of Git-based platforms such as GitHub or Azure DevOps for version control, branching strategies, and CI/CD pipelines
- Experience with data orchestration platforms such as Apache Airflow and/or Astronomer for pipeline scheduling, monitoring, and dependency management
- Strong understanding of data warehousing concepts, ETL/ELT patterns, and data integration best practices
- Excellent communication and collaboration skills with the ability to work cross-functionally in a fast-paced environment
Preferred Qualifications:
- Industry certifications demonstrating proficiency in data engineering (e.g., Databricks Certified Data Engineer, Azure Data Engineer Associate DP-203, Snowflake SnowPro Core, Google Professional Data Engineer, AWS Data Engineer Associate)
- Experience with a major cloud platform (Azure, AWS, or GCP) including infrastructure-as-code and cloud-native data services
- Prior experience in sports, entertainment, media, or live events industries
- Familiarity with streaming and real-time data technologies (Kafka, Event Hubs, Spark Structured Streaming)
- Experience with data governance, cataloging, and lineage tools (Unity Catalog, Purview, Collibra)
- Exposure to machine learning pipelines and MLOps practices
- Experience with containerization (Docker, Kubernetes) and microservices architecture.
