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Data Engineer

Remote (United States), Canada

Live, first seen by us 52 days ago
Pay
Pay not listed
Work setting
Remote — work setting source
Listed location: Remote (United States), Canada
Read the full posting
Employment
Full-time — employment source
Employment type in the employer’s posting: SALARIED_FT
From the employer’s posting

What you’ll work on

Full 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

From 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. 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.

What you’ll bring

All qualifications

Core experience

  • 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.
Qualification wording
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.

Benefits in the posting

Full benefits wording
  • Competitive Salary and Bonus Plan
  • Comprehensive health insurance plan
  • Retirement savings plan (401k) with company match
  • Remote working environment
  • Generous paid holiday schedule - 13 in total including Monday after the Super Bowl
  • The total compensation package for this position may also include annual performance bonus, benefits and/or other applicable incentive compensation plans.

From the employer’s posting.

Apply at SumerSports

Posting record

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Status in our records
Live
First seen by us
Sep 13, 202652 days ago
Recorded sightings
29
Last seen by us
Nov 4, 2026
Employer says posted
Sep 10, 2026
Reposted
once since Aug 16, 2026

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.

How this job compares

Time since we first saw it

Day 52

Similar jobs usually come down between day 6 and day 54.

From 11,032 similar postings that came down. Coming down is not always a hire.

SumerSports is hiring

2 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • 100% of open jobs were reposted
Hiring record

See how this role fits your experience

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.

  • Python
  • SQL
  • AWS
  • Azure
  • Databricks
  • Docker
  • GCP
  • Kafka
  • Kubernetes
  • Prefect
  • Spark
  • Airflow
  • Dagster
  • Luigi
Where the posting names them
- 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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Demand for Data Engineer roles

New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.

Weekly new Data Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 839, the most 1,314, and the latest week had 1,086.
August 17Last week

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.

About the role

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.

US government figures for database architects

Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs

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.

Job description

View original posting ↗

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

  • 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.


Qualifications

  • 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).


Preferred

  • 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.


Benefits

  • Competitive Salary and Bonus Plan
  • Comprehensive health insurance plan
  • Retirement savings plan (401k) with company match
  • Remote working environment
  • A flexible, unlimited time off policy
  • Generous paid holiday schedule - 13 in total including Monday after the Super Bowl


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
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