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Data Engineer Sports Analytics Jobs in Normal, IL

1 point system

Senior Data Engineer

Normal, IL · On-site

$103K - $140K/yr

As a Sr. Data Engineer, you will help build and operate the data foundation that powers analytics across programs and sites. You will design and maintain scalable ingestion pipelines, develop well ...

Kemper

Senior Data Systems Analyst

Bloomington, IL

Partner with engineering and operational teams to prioritize remediation activities. * Track ... Bachelor's degree in Information Systems, Computer Science, Data Analytics, Business Analytics, or ...

Seedway, LLC

Data Enablement Specialist I

Bloomington, IL · On-site

$69 - $95/hr

Collaborate with data engineers and IT teams to integrate Tableau with data warehouses and other business systems. Performance Optimization * Analyze and optimize dashboard performance and query ...

Kemper

Data Science Manager

Bloomington, IL · Hybrid

Graduate degree in Mathematics, Statistics, Engineering, or other STEM field with 6-8 years of experience in insurance industry or related industry in a data science/analytics environment. * PhD in ...

Steampunk

MLOps Engineer

Bloomington, IL · On-site

$115 - $150/hr

TheMLOpsEngineer works closely with Data Scientists, AI Developers, Data Engineers, and cloud ... Support troubleshooting and root-cause analysis of pipeline issues, infrastructure problems, or ...

PB consulting

MDM ETL Test Engineer

Yuton, IL · On-site

Identify data validation scenarios and ensure comprehensive test coverage. * Collaborate with business analysts, developers, and QA teams to understand business requirements and data flows.

PB consulting

MDM ETL Test Engineer

Ellsworth, IL · On-site

Identify data validation scenarios and ensure comprehensive test coverage. * Collaborate with business analysts, developers, and QA teams to understand business requirements and data flows.

PB consulting

MDM ETL Test Engineer

Downs, IL · On-site

Identify data validation scenarios and ensure comprehensive test coverage. * Collaborate with business analysts, developers, and QA teams to understand business requirements and data flows.

PB consulting

MDM ETL Test Engineer

Danvers, IL · On-site

Identify data validation scenarios and ensure comprehensive test coverage. * Collaborate with business analysts, developers, and QA teams to understand business requirements and data flows.

PB consulting

MDM ETL Test Engineer

Heyworth, IL · On-site

Identify data validation scenarios and ensure comprehensive test coverage. * Collaborate with business analysts, developers, and QA teams to understand business requirements and data flows.

PB consulting

MDM ETL Test Engineer

Mclean, IL · On-site

Identify data validation scenarios and ensure comprehensive test coverage. * Collaborate with business analysts, developers, and QA teams to understand business requirements and data flows.

PB consulting

MDM ETL Test Engineer

Hudson, IL · On-site

Identify data validation scenarios and ensure comprehensive test coverage. * Collaborate with business analysts, developers, and QA teams to understand business requirements and data flows.

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Showing results 1-20

All JobsData Engineer Sports Analytics JobsData Engineer Sports Analytics Jobs in Normal, IL

Data Engineer Sports Analytics information

See Normal, IL salary details

$43.5K

$126.8K

$173.5K

How much do data engineer sports analytics jobs pay per year?

As of Aug 27, 2026, the average yearly pay for data engineer sports analytics in Normal, IL is $126,819.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,900.00 and $134,400.00 per year, depending on experience, location, and employer.

What does a data engineer in sports analytics do?

A Data Engineer in Sports Analytics designs, builds, and maintains the infrastructure and systems that collect, store, and process large volumes of sports-related data. They ensure data pipelines are efficient and reliable so that analysts and data scientists can access accurate information for player performance analysis, game strategy, and business decisions. Their work involves integrating data from various sources, optimizing databases, and implementing best practices in data security and quality, all within the context of the sports industry.

What are the key skills and qualifications needed to thrive as a data engineer in sports analytics?

To thrive as a Data Engineer in Sports Analytics, you need a strong background in computer science, data modeling, and database management, typically supported by a relevant degree and experience with large data sets. Familiarity with tools and technologies such as SQL, Python, Spark, cloud platforms (AWS, Azure), and ETL pipelines is essential, and certifications in these areas can be advantageous. Excellent problem-solving, teamwork, and communication skills help you collaborate with analysts, coaches, and stakeholders to translate data into actionable insights. These competencies ensure the efficient collection, processing, and delivery of high-quality sports data that drive performance analysis and competitive advantage.

How does a data engineer in sports analytics typically collaborate with data scientists and analysts on a project?

As a Data Engineer in Sports Analytics, you’ll regularly work alongside data scientists and analysts to ensure high-quality, reliable data is available for modeling and analysis. Your responsibilities often include building and maintaining data pipelines, transforming raw sports data into usable formats, and optimizing data storage for performance. Effective communication is key, as you’ll need to understand the analytical requirements and adjust pipelines or data sources accordingly. Collaboration often happens through regular meetings, shared documentation, and close feedback loops to align on project goals and data needs.

What is the difference between Data Engineer Sports Analytics vs Data Analyst Sports Analytics?

AspectData Engineer Sports AnalyticsData Analyst Sports Analytics
Primary FocusBuilding and maintaining data pipelines, infrastructure, and databasesAnalyzing data, generating reports, and providing insights
Skills & CertificationsSQL, Python, data warehousing, cloud platformsExcel, SQL, statistical analysis, visualization tools
Work EnvironmentData engineering teams, IT infrastructureBusiness teams, sports analytics departments
Industry UsageSports organizations, tech companies supporting sports dataSports teams, media outlets, betting companies

While Data Engineer Sports Analytics focuses on building and maintaining the data infrastructure necessary for sports data analysis, Data Analyst Sports Analytics concentrates on interpreting that data to generate actionable insights. Both roles are essential in sports analytics but serve different functions within the data ecosystem.

What are popular job titles related to Data Engineer Sports Analytics jobs in Normal, IL?

For Data Engineer Sports Analytics jobs in Normal, IL, the most frequently searched job titles are:

What job categories do people searching Data Engineer Sports Analytics jobs in Normal, IL look for?

The top searched job categories for Data Engineer Sports Analytics jobs in Normal, IL are:

What cities near Normal, IL are hiring for Data Engineer Sports Analytics jobs?

Cities near Normal, IL with the most Data Engineer Sports Analytics job openings:

Data Engineer Sports Analytics jobs near you

Senior Data Engineer

1 point system

Normal, IL • On-site

$103K - $140K/yr

Contractor

Re-posted 12 days ago

Job description

Job Description

As a Sr. Data Engineer, you will help build and operate the data foundation that powers analytics across programs and sites. You will design and maintain scalable ingestion pipelines, develop well-modeled datasets, and ensure data quality and reliability across critical systems.

This role sits at the intersection of operational systems, modern data platform engineering, and analytics enablement, helping convert raw operational signals into governed, high-value datasets that power analytics applications, core metrics, and future AI-driven insights.

Roles and Responsibilities

  • Design, build, and operate data ingestion pipelines from operational, quality, test, and service systems into Databricks using Fivetran/AWS/Databricks and standardized ELT patterns.
  • Develop and maintain dbt models for operational, quality, and ramp metrics, following our standard patterns for analytics.
  • Set up and maintain data quality checks, audits, and monitoring for freshness, completeness, and contract compliance across key pipelines.
  • Optimize Databricks jobs, Fivetran connectors, and dbt runs for performance, cost, and reliability, including orchestration, alerting, and runbooks.
  • Collaborate with analytics engineers and product teams to turn models into highvalue data products powering analytics applications.
  • Contribute to the semantic layer and catalog so GenAI agents and self-service tools can reliably discover and query operational data.
  • Drive improvements in upstream systems and schemas to reduce data issues at the source.

Required Qualifications

  • 5+ years of experience in Data Engineering, Analytics Engineering, or Software Engineering working with production data systems.
  • Strong expertise in SQL and Python for building scalable data pipelines and transformations.
  • Hands-on experience building ELT pipelines using modern cloud data platforms (Databricks strongly preferred).
  • Deep experience with dbt, including model development, testing, documentation, and CI/CD integration.
  • Experience with managed ingestion tools such as Fivetran, Airbyte, or similar.
  • Experience designing and operating production-grade data pipelines with monitoring and observability.
  • Strong collaboration skills and ability to partner with engineering teams, analysts, and operational stakeholders.
  • Bachelors or Master’s degree in Computer Science, Engineering, Mathematics, or related field, or equivalent practical experience.

Preferred Qualifications

  • Experience working with manufacturing, MES, quality, or operational data.
  • Familiarity with Fivetran connector management and ingestion architecture.
  • Experience with data contracts and schema governance.
  • Experience building semantic layers or governed analytical datasets.
  • Exposure to modern analytics tools (Hex, Tableau, Power BI, or similar).
  • Experience enabling AI or advanced analytics use cases on top of operational data.
  • Knowledge of streaming or near-real-time data pipelines.