Key skills
PythonSQLAIMLDeep LearningMLOpsETLELTData WarehousingAnalyticsDatabricksSparkKafkaAirflowAWSAzureGCPKubernetesDockerGitHub ActionsGitHubCI/CD
About this role
SumerSports is a leading football intelligence technology company that specializes in providing an innovative suite of products for football fans and NFL clubs. As a Data Engineer, you will design, build, and maintain the data pipelines that power deep learning and AI-driven products, ensuring data is reliable, discoverable, and scalable across multiple sports.
Responsibilities:
- Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Dagster
- Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads
- Collaborate with ML and AI teams to deliver high-quality datasets for training, evaluation, and production features
- Model and maintain structured data assets (Delta, Parquet, Iceberg) for reliability, versioning, and lineage tracking
- Implement orchestration and monitoring — schedule jobs, track dependencies, and automate recovery from failures
- Ensure data quality and compliance through validation frameworks, schema enforcement, and audit logging
- Contribute to data platform evolution — evaluate tools, standardize best practices, and improve developer experience
- Support performance and cost optimization across compute, storage, and orchestration systems
Requirements:
- 3–6 years of experience as a Data Engineer or ETL Developer in a production environment
- Proficiency in Python and SQL; strong familiarity with Databricks, Spark, or equivalent big-data frameworks
- Experience with workflow orchestration tools such as Airflow, Dagster, Luigi or Prefect
- Deep understanding of data modeling, data warehousing, and distributed data processing
- Knowledge of modern data lakehouse architectures (Delta, Parquet, Iceberg)
- Familiarity with CI/CD, GitHub Actions, and data pipeline testing frameworks
- Comfort working in a cross-functional environment with ML, product, and analytics teams
- Experience with sports, telemetry, or sensor data pipelines
- Familiarity with streaming frameworks (Kafka, Spark Structured Streaming, Flink)
- General knowledge of American football, the NFL, and college football
- Background in data governance, lineage, and observability tools (Monte Carlo, Great Expectations, Unity Catalog, OpenLineage)
- Experience with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
- Exposure to best practices in machine-learning model management and MLOps