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Day 52
Similar jobs usually come down between day 6 and day 54.
From 11,035 similar postings that came down. Coming down is not always a hire.
Remote (United States), Canada
Live, first seen by us 52 days agoListed location: Remote (United States), CanadaRead the full posting
Employment type in the employer’s posting: SALARIED_FTFrom the employer’s posting
Our data engineering team is the foundation of this system, ensuring data is accurate, fast, and always available for our models and AI applications.
As a Data Engineer, you’ll design, build, and maintain the data pipelines that power our deep learning, video and LLM systems.
Build and operate ingestion, cleaning, and transformation pipelines using Databricks, Airflow, or Kubernetes
Develop batch and streaming ETL/ELT workflows in Python and SQL
Develop RAG and vector search retrieval pipelines over stats and unstructured sources
Model and maintain versioned data assets in Delta, Parquet, or Iceberg
Schedule jobs, track dependencies, and automate recovery from failures
Our data engineering team is the foundation of this system, ensuring data is accurate, fast, and always available for our models and AI applications.
As a Data Engineer, you’ll design, build, and maintain the data pipelines that power our deep learning, video and LLM systems. You’ll work across ingestion, transformation, and orchestration layers — from real-time feeds to analytics-ready datasets. Your mission is to make data reliable, discoverable, and scalable for use by model training, analytics, and AI-driven products across multiple sports. You’ll collaborate closely with our MLOps, and Sports Data teams to ensure seamless integration between data and AI.
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.
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.
3–8 years of experience as a Data Engineer or ETL Developer in a production environment.
Familiarity with streaming frameworks and event driven data processing (Kafka, Spark Structured Streaming, Flink).
Proficiency in Python and SQL; strong familiarity with Databricks, Spark, or equivalent big-data frameworks.
Experience designing semantic layers or metric definitions consumed by AI and BI tools.
Experience with workflow orchestration tools such as Airflow, Dagster, Luigi or Prefect.
Deep understanding of data modeling, data warehousing, and distributed data processing.
From the employer’s posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
Day 52
Similar jobs usually come down between day 6 and day 54.
From 11,035 similar postings that came down. Coming down is not always a hire.
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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.
- 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.
- 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).
- 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). Preferred
Responsibilities - 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.
- 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.
- 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.
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New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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.
Our data-driven platform empowers teams with insights and tools to make informed decisions within salary cap constraints. The platform also serves the NCAA, offering insights around the transfer portal and more.
What sets us apart is our unique blend of big tech talent, data scientists, and former NFL personnel, who have a combined 600+ years of NFL experience. Our domain knowledge is augmented by AI and machine learning technologies to create a unique view into many aspects of Football.
Position Summary
Our data engineering team is the foundation of this system, ensuring data is accurate, fast, and always available for our models and AI applications.
As a Data Engineer, you’ll design, build, and maintain the data pipelines that power our deep learning, video and LLM systems. You’ll work across ingestion, transformation, and orchestration layers — from real-time feeds to analytics-ready datasets. Your mission is to make data reliable, discoverable, and scalable for use by model training, analytics, and AI-driven products across multiple sports. You’ll collaborate closely with our MLOps, and Sports Data teams to ensure seamless integration between data and AI.
Responsibilities
Qualifications
Preferred
Benefits
SumerSports is committed to fair and equitable compensation practices.
Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
The total compensation package for this position may also include annual performance bonus, benefits and/or other applicable incentive compensation plans.
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Remote (United States), Canada
- Retirement savings plan (401k) with company match - Remote working environment - A flexible, unlimited time off policy
No clear work-authorization passage found. Eligibility is unconfirmed.