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Data Engineer Sports Analytics Jobs in Oklahoma (NOW HIRING)

Data Engineer

Oklahoma City, OK · On-site

$106K - $127K/yr

JOB OVERVIEW The mission of the Data Analytics Engineer at Warwick Energy is to bridge the gap between data engineering and business intelligence (BI) teams seamlessly. In this role, you will convert ...

Data Engineer

Oklahoma City, OK · On-site

$106K - $127K/yr

JOB OVERVIEW The mission of the Data Analytics Engineer at Warwick Energy is to bridge the gap between data engineering and business intelligence (BI) teams seamlessly. In this role, you will convert ...

Data Engineer

Oklahoma City, OK · On-site

$106K - $127K/yr

S. now and in the future without sponsorship We're partnering with a client on a direct-hire Data Engineering role supporting a growing analytics and data platform. This position is ideal for someone ...

Data Engineer

Oklahoma City, OK · On-site

$106K - $127K/yr

S. now and in the future without sponsorship We're partnering with a client on a direct-hire Data Engineering role supporting a growing analytics and data platform. This position is ideal for someone ...

Data Engineer

Oklahoma City, OK · On-site

$106K - $127K/yr

S. now and in the future without sponsorship We're partnering with a client on a direct-hire Data Engineering role supporting a growing analytics and data platform. This position is ideal for someone ...

Data Engineer

Edmond, OK · On-site

$125K - $150K/yr

Data Engineer Direct Hire * Must be authorized to work in the United States now and in the future ... Create curated datasets to support application developers, business teams, and analytics users.

Senior Data Engineer

Tulsa, OK · On-site +1

$96K - $131K/yr

We are seeking a Senior Data Engineer with deep expertise in data warehousing, ETL pipeline ... About SmartLight Analytics SmartLight Analytics was formed by a group of industry insiders who ...

Data Engineer

Edmond, OK · On-site

$125K - $150K/yr

Data Engineer Direct Hire * Must be authorized to work in the United States now and in the future ... Create curated datasets to support application developers, business teams, and analytics users.

Data Engineer

Tulsa, OK

$104K - $125K/yr

The team's work spans Data & Systems, CRM Platform Solutions, and Analytics-building the data ... This role requires strong hands-on experience in Python-based data engineering, cloud data ...

Data Engineer

Tulsa, OK · On-site

$104K - $125K/yr

The team's work spans Data & Systems, CRM Platform Solutions, and Analytics-building the data ... This role requires strong hands-on experience in Python-based data engineering, cloud data ...

Data Engineer II

Tulsa, OK · On-site

$120/hr

Data Engineer II/III Location: Tulsa, Oklahoma Salary: $120-135k Position is not eligible for ... Connect to and analyze data from a variety of source systems to assess data quality, structure, and ...

Data Engineer II

Tulsa, OK · On-site

$120/hr

Data Engineer II/III Location: Tulsa, Oklahoma Salary: $120-135k Position is not eligible for ... Connect to and analyze data from a variety of source systems to assess data quality, structure, and ...

Data Engineer

Tulsa, OK · On-site

$104K - $125K/yr

Proficiency in leveraging AI enabled tools, such as Microsoft Copilot, to improve productivity and analysis. A plus will be experience in AWS related to data engineering. Experience required: * 8+ ...

Data Engineer -Mauritius

Blair, OK · On-site

$101K - $122K/yr

The Data Engineer facilitates the flow of oftentimes massive quantities of raw files from various ... Analyze documentation and propose data extraction solutions * Write code that uses APIs to download ...

Data Engineer II

Tulsa, OK · On-site

$99K - $119K/yr

Data Engineer Company Overview Our client is a respected leader in the retail and consumer services ... Analyze and profile data from diverse sources to determine integration requirements and data ...

Data Engineer

Edmond, OK · On-site

$103K - $124K/yr

The Data Engineer will be responsible for driving data awareness and providing the analytics teams with actionable data. This role will utilize systems-thinking, will require broad technical ...

Data Engineer

Edmond, OK · On-site

$103K - $124K/yr

The Data Engineer will be responsible for driving data awareness and providing the analytics teams with actionable data. This role will utilize systems-thinking, will require broad technical ...

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Data Engineer Sports Analytics information

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 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.

What are popular job titles related to Data Engineer Sports Analytics jobs in Oklahoma?

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

What cities in Oklahoma are hiring for Data Engineer Sports Analytics jobs?

Cities in Oklahoma with the most Data Engineer Sports Analytics job openings:

Infographic showing various Data Engineer Sports Analytics job openings in Oklahoma as of July 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution.

$106K - $127K/yr

Full-time

Posted 7 days ago


Job description

JOB OVERVIEW

The mission of the Data Analytics Engineer at Warwick Energy is to bridge the gap between data engineering and business intelligence (BI) teams seamlessly. In this role, you will convert raw data into well-structured, accessible datasets and design scalable data models that enable meaningful analytics and reporting.

A critical part of this role involves applying data engineering principles to build and maintain trusted, high-quality data objects within the data warehouse. By ensuring the accuracy, consistency, and reliability of these data assets, you will create a strong foundation for scalable analytics and BI solutions. Your work will enable the organization to leverage a single source of truth for decision-making, fostering confidence in data-driven insights.

As a detail-oriented professional, you will ensure high levels of accuracy and thoroughness in data preparation and documentation. Your problem-solving skills will be essential as you apply analytical thinking to tackle complex challenges in data workflows and in shaping the data.

Collaboration is key in this role, as you will communicate effectively across teams and foster a cooperative working environment. An eagerness for continuous improvement will drive you to learn and implement efficient data solutions and creating robust data products that power BI applications, we encourage you to join our team.

 

KEY JOB RESPONSIBILITIES

  • Data Transformation: Bridge data engineering and BI teams to convert raw data into structured, actionable datasets.
  • Data Modeling: Design scalable data models and semantic layers to optimize BI tool analysis.
  • Strengthen Data Infrastructure: Collaborate with data engineers to design and maintain robust data models for BI projects.
  • Workflow Development: Develop and maintain version-controlled analytics workflows using tools like DBT.
  • Business Alignment: Ensure analytical outputs meet business objectives and are accessible to all users.
  • Technical Documentation: Create clear documentation for data models, workflows, and analytics solutions for both technical and non-technical stakeholders.
  • Collaboration: Work with cross-functional teams to support data-driven decision-making and promote best practices in data analytics.

REQUIRED SKILLS

  • Oil and Gas Industry Experience: At least 5 years of hands-on experience in various aspects of the oil and gas industry, understanding its unique data challenges.
  • SQL Expertise: Proficient in writing complex SQL queries and designing data models that integrate diverse datasets. Experience with CTEs, advanced formulas, and windowing functions for sophisticated data transformations.
  • Documentation Skills: Capable of producing clear and detailed documentation for data models, workflows, ELT processes, and analytics solutions to support team collaboration.
  • Data Modeling Principles: Deep knowledge of data modeling concepts and best practices, including creating semantic layers for BI tools.
  • BI Tools Experience: At least 5 years of experience in building data models for BI tools like Power BI and Spotfire, understanding data ingestion and transformation for optimal data display.
  • Collaborative Experience: Proven ability to work effectively with functional business teams, data engineering teams, and analytics teams to align data solutions with business needs.
  • Scalable Workflows: Proven ability to design and implement scalable workflows and optimize data queries for performance and efficiency.
  • Version Control Knowledge: Familiar with using version control systems like Git and Azure DevOps to manage and track changes in data models and scripts.

DESIRED SKILLS

  • Snowflake and Microsoft Fabric: Proficient in using Snowflake and Microsoft Fabric for data warehousing and management.
  • Cloud Platforms: Knowledgeable about cloud platforms like Snowflake, Azure, AWS, and GCP for data storage, processing, and analytics.
  • Python for Data Manipulation: Competent in using Python for data manipulation, analysis, and automation tasks.
  • Data Governance: Well-versed in data governance principles and compliance requirements, ensuring data quality and security.

    REQUIRED SKILLS

  • Oil and Gas Industry Experience: At least 5 years of hands-on experience in various aspects of the oil and gas industry, understanding its unique data challenges.
  • SQL Expertise: Proficient in writing complex SQL queries and designing data models that integrate diverse datasets. Experience with CTEs, advanced formulas, and windowing functions for sophisticated data transformations.
  • Documentation Skills: Capable of producing clear and detailed documentation for data models, workflows, ELT processes, and analytics solutions to support team collaboration.
  • Data Modeling Principles: Deep knowledge of data modeling concepts and best practices, including creating semantic layers for BI tools.
  • BI Tools Experience: At least 5 years of experience in building data models for BI tools like Power BI and Spotfire, understanding data ingestion and transformation for optimal data display.
  • Collaborative Experience: Proven ability to work effectively with functional business teams, data engineering teams, and analytics teams to align data solutions with business needs.
  • Scalable Workflows: Proven ability to design and implement scalable workflows and optimize data queries for performance and efficiency.
  • Version Control Knowledge: Familiar with using version control systems like Git and Azure DevOps to manage and track changes in data models and scripts.
  • DESIRED SKILLS

  • Snowflake and Microsoft Fabric: Proficient in using Snowflake and Microsoft Fabric for data warehousing and management.
  • Cloud Platforms: Knowledgeable about cloud platforms like Snowflake, Azure, AWS, and GCP for data storage, processing, and analytics.
  • Python for Data Manipulation: Competent in using Python for data manipulation, analysis, and automation tasks.
  • Data Governance: Well-versed in data governance principles and compliance requirements, ensuring data quality and security.