Data Engineer (Sports Analytics & AI)

Location: Remote

Company

hirify.global builds decision-making technology for American football used by professional and college teams and fans, combining player-value, roster-construction, and in-game strategy models.

What you will do

• Build and operate ingestion, cleaning, transformation, batch, and near-real-time pipelines using Databricks, Airflow, Kubernetes, Python, and SQL.
• Create analytics-ready datasets and tools for machine learning models, analysts, AI products, and autonomous agents.
• Build RAG and vector-search pipelines across structured statistics, scouting notes, and video metadata.
• Design Delta, Parquet, and Iceberg assets with versioning, lineage, and reliability.
• Own scheduling, dependency tracking, monitoring, automated failure recovery, validation frameworks, schema contracts, and audit logging.
• Evaluate data-platform tooling and optimize performance and cost across compute, storage, and orchestration.

Requirements

• 3–8 years of experience building and running production data pipelines as a data engineer or ETL developer.
• Strong Python and SQL skills, with hands-on Databricks, Spark, or a comparable distributed-processing framework.
• Production experience with Airflow, Dagster, Prefect, or Luigi.
• Solid data modelling and warehousing knowledge, with comfort working with modern lakehouse architectures.
• Experience with CI/CD, GitHub Actions, infrastructure as code, and testing data pipelines.
• Experience with AWS, GCP, or Azure, plus Docker and Kubernetes.
• Some exposure to LLM data work, including text-to-SQL, RAG, natural-language analytics, or agent interfaces such as MCP.
• Ability to work directly with machine learning, product, and analytics stakeholders and explain trade-offs clearly.

Culture & Benefits

• Remote work with direct collaboration across machine learning, sports data, product, and analytics teams.
• Work on decision-making technology for professional and college American football.

Hiring process

• Brief non-technical conversation about background, career aspirations, communication, teamwork, and decision-making.
• 60–90 minute technical interview focused on challenges relevant to the requirements.
• Final client interview with a technical discussion, followed by an offer for successful candidates.