~6d ago

Data Engineer

Canadamid

Data EngineerData

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Quick Summary

Key Responsibilities

schedule jobs, track dependencies, and automate recovery from failures. Ensure data quality and compliance through validation frameworks, schema enforcement, and audit logging.

Technical Tools

Data EngineerData

SumerSports is a leading football intelligence technology company that specializes in providing an innovative suite of products for football fans and NFL clubs. We are a collection of executives, engineers, data scientists, and visionaries from NFL clubs, technology startups, finance, and academia.

Responsibilities

~1 min read

→Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Kubernetes.
→Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads.
→Partner with ML/AI teams to make datasets and tools discoverable and safe for autonomous agents, including evaluation and guardrails for AI-generated queries.
→Develop retrieval pipelines (RAG, vector search) over structured stats and unstructured sources (scouting notes, video metadata) to power AI applications.
→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

~1 min read

3–8 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.
Familiarity with CI/CD, GitHub Actions, Infrastructure as Code, and data pipeline testing frameworks.
Comfort working in a cross-functional environment with ML, product, and analytics teams.
Exposure to LLM-powered data tools: text-to-SQL, RAG, agent/tool interfaces (e.g. MCP), or natural-language analytics.
Previous work with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Docker, Kubernetes).

Nice to Have

~1 min read

Previous experience with sports, telemetry, or sensor data pipelines.
Familiarity with streaming frameworks and event driven data processing (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 designing semantic layers or metric definitions consumed by AI and BI tools.
Exposure to best practices in machine-learning model management and MLOps.