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Nba Data Analyst Jobs (NOW HIRING)

AEG

Data Engineer

San Antonio, TX · On-site

$103K - $124K/yr

SS&E owns and operates the San Antonio Spurs (NBA), Austin Spurs (NBA G-League), and the San ... Bachelor's or Master's Degree in Computer Science, Engineering, Data Analytics, Information Systems ...

AEG

BI Analyst- Revenue

Atlanta, GA · On-site

$85K - $120K/yr

... NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 ... This data-centric role is vital for building and maintaining analytics tools and workflows.

PrizePicks

BI Analyst- Revenue

Atlanta, GA · On-site +1

$85K - $120K/yr

... NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 ... This data-centric role is vital for building and maintaining analytics tools and workflows.

AEG

Data & Analytics Lead

Calabasas, CA · On-site

... NBA, NFL, NCAA, and NASCAR. We're also proud to partner with some of the most iconic teams and ... The Data & Analytics Lead is the "intelligence engine" for the entire organization. We are already ...

Benson Enterprises

Consumer Insights Analyst

Metairie, LA · On-site

... NBA. You understand and appreciate that New Orleans is a unique city with so much to offer, and you ... Analyze local and regional market data from Bloomberg, Moody's Analytics, Census, Bureau of Labor ...

AEG

Consumer Insights Analyst

Metairie, LA · On-site

... NBA. You understand and appreciate that New Orleans is a unique city with so much to offer, and you ... Analyze local and regional market data from Bloomberg, Moody's Analytics, Census, Bureau of Labor ...

AEG

Associate Basketball Analyst, Coaching

El Segundo, CA · On-site

$115K - $125K/yr

Strong knowledge of NBA basketball * Hands-on experience with basketball analytics; college or NBA environments preferred * Expertise with large data sets, statistical analysis (e.g. Python), and ...

Swish Analytics

Trading Analyst

San Francisco, CA · Remote

$80K/yr

Independently analyze betting trends in a scientific process, turning data into quantifiable ... Passion and intimate knowledge of the NBA, NFL, MLB, NHL, and NCAA Basketball + Football * Strong ...

Swish Analytics

Trading Analyst

San Francisco, CA · On-site +1

$80K/yr

Independently analyze betting trends in a scientific process, turning data into quantifiable ... Passion and intimate knowledge of the NBA, NFL, MLB, NHL, and NCAA Basketball + Football * Strong ...

TicketManager

Data & Analytics Lead

Calabasas, CA · On-site

$80K - $100K/yr

... NBA, NFL, NCAA, and NASCAR. We're also proud to partner with some of the most iconic teams and ... The Data & Analytics Lead is the "intelligence engine" for the entire organization. We are already ...

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Nba Data Analyst information

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How much do nba data analyst jobs pay per year?

As of Jun 21, 2026, the average yearly pay for nba data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What does an NBA data analyst do?

An NBA data analyst collects, analyzes, and interprets basketball data to provide insights on player performance, team strategies, and game trends. They use statistical tools and software to support coaching decisions, player development, and fan engagement efforts.

What are the key skills and qualifications needed to thrive in the Nba Data Analyst position, and why are they important?

To thrive as an NBA Data Analyst, you need a strong background in statistics, data analysis, and basketball analytics, typically supported by a relevant degree in mathematics, statistics, or data science. Familiarity with analytical tools like SQL, Python, R, and data visualization software such as Tableau is essential, and experience using basketball analytics databases (like Synergy Sports or Second Spectrum) is highly valued. Attention to detail, effective communication, and the ability to collaborate with coaches and front-office staff are critical soft skills. These qualifications enable analysts to translate complex data into actionable insights that improve team performance and strategy.

What are the typical duties and responsibilities of an NBA Data Analyst during the basketball season?

NBA Data Analysts play a key role throughout the basketball season by collecting, cleaning, and analyzing large sets of game, player, and team data. They generate reports and visualizations that help coaches and executives make informed decisions about strategy, player performance, and opposition analysis. Collaboration is frequent, as analysts regularly meet with coaching staff, scouts, and management to present findings and answer data-driven questions. Their work environment is fast-paced, especially during games and playoffs, with rapid turnaround on actionable insights. The role often requires flexibility in hours and a proactive approach to problem-solving, staying up-to-date with the latest analytical methodologies and basketball trends.

How much does an NBA analyst get paid?

NBA data analysts typically earn between $50,000 and $100,000 annually, depending on experience, location, and the organization. Entry-level analysts may start at lower salaries, while experienced professionals with advanced skills in data analysis and sports statistics can earn higher compensation.

How to become an NBA data analyst?

To become an NBA data analyst, you typically need a bachelor's degree in statistics, data science, or a related field, along with strong skills in data analysis tools like Excel, SQL, and programming languages such as Python or R. Experience with sports analytics, understanding of basketball metrics, and familiarity with visualization software like Tableau are also valuable. Gaining internships or entry-level roles in sports or data analysis can help build relevant experience for this role.

Do NBA teams have data scientists?

Yes, many NBA teams employ data scientists and analysts to evaluate player performance, develop strategies, and enhance team decision-making. These professionals often use statistical tools, machine learning, and data visualization to support coaching and management staff.

What is an NBA Data Analyst job?

An NBA Data Analyst is responsible for collecting, analyzing, and interpreting basketball data to provide insights that help teams make informed decisions. They work with statistics related to player performance, game strategy, and opponent tendencies, often using advanced analytics and data visualization tools. Their insights assist coaching staff, front office executives, and scouts in improving team performance, player acquisitions, and game strategies. Strong skills in programming, data modeling, and basketball knowledge are essential for this role.

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Nba Data Analyst jobs near you

Data Engineer

AEG

San Antonio, TX • On-site

$103K - $124K/yr

Full-time

Posted 10 days ago

Job description

In order to be considered for this role, after clicking "Apply Now" above and being redirected, you must fully complete the application process on the follow-up screen.
At Spurs Sports & Entertainment (SS&E), we work in service of something bigger than ourselves.
To us it is so much more than just a game or concert. It takes all the members of our Spurs team to harness the power of sports and entertainment to create moments that excite, memories that endure, and connections that strengthen our community. SS&E owns and operates the San Antonio Spurs (NBA), Austin Spurs (NBA G-League), and the San Antonio FC (USL), and manages day-to-day operations of the Frost Bank Center, Toyota Field and STAR Complex.
We know that our people are our greatest asset as an organization. We aspire to provide our teams with meaningful work, to live our values -Integrity, Success & Caring - day-to-day in what we do and foster an inclusive culture for our 1K+ employee workforce.
We are seeking a highly skilled, future-ready Data Engineer to help build the data platform that powers trusted Business Intelligence today and AI-enabled experiences tomorrow. This role is grounded in strong data engineering fundamentals - scalable pipelines, clean data modeling, reliable integrations, database performance, governance, and data quality - while also advancing the organization's ability to support front-end data products, MCPs, and AI-agent workflows. The ideal candidate brings deep Python capability, disciplined GitHub-based development, deployment awareness, excellent technical documentation habits, and strong ownership of production-quality data solutions. SS&E is a tech-forward organization that believes in the responsible use of AI, with responsibility and accountability remaining with the individual. This position encourages the responsible use of AI-assisted engineering tools such as OpenAI Codex, Claude Code, or comparable tools under SS&E's enterprise agreements to support development, testing, refactoring, debugging, documentation, and codebase understanding; however, these tools should not be relied upon as a substitute for sound data engineering judgment, clean architecture, secure coding practices, or hands-on technical ownership.
Who You Are: Minimum Qualifications
  • Bachelor's or Master's Degree in Computer Science, Engineering, Data Analytics, Information Systems, or a related field.
  • 1-2 years of experience as a Data Engineer, Analytics Engineer, Software Engineer with data focus, or similar technical role building data pipelines, models, and data platforms.
  • Strong foundation in data engineering fundamentals, including ETL/ELT pipelines, data modeling, relational databases, API integrations, data validation, orchestration concepts, and scalable data architecture.
  • Expertise in Python, including experience building production-ready scripts, data pipelines, automation workflows, APIs, backend services, or data processing frameworks.
  • Proficiency in SQL, including SQL-based data modeling, query optimization, transformation logic, and troubleshooting.
  • Hands-on experience with SQL Server, Azure SQL Managed Instance, Snowflake, or other relational database systems.
  • Experience extracting, transforming, and integrating data from complex APIs, SaaS platforms, operational systems, and external data sources.
  • Experience using GitHub for version control, pull requests, code review, branching strategies, release documentation, and collaborative development workflows.
  • Experience creating and maintaining clear technical documentation for data pipelines, data models, APIs, deployment steps, system dependencies, support procedures, and runbooks.
  • Knowledge of how data infrastructure supports BI tools, React-based front ends, internal applications, and AI-enabled workflows.
  • Familiarity with Azure or AWS cloud services used for data storage, compute, integration, deployment, and monitoring, with preference for Azure-based data environments.
  • Experience developing governed datasets, curated reporting layers, or datasets for Business Intelligence, visualization, and operational analytics use cases.
  • Strong analytical, troubleshooting, and problem-solving skills with the ability to improve existing systems without disrupting business operations.
  • Effective communication skills and ability to collaborate with technical and non-technical stakeholders.

Preferred Qualifications:
  • Experience designing AI-ready data infrastructure, MCPs, governed context layers, or data services that support internal AI agents and automation.
  • Experience using OpenAI Codex, Claude Code, or comparable AI-assisted engineering tools in GitHub-connected, CLI, IDE, or deployment-adjacent workflows under appropriate enterprise usage standards.
  • Experience with NoSQL databases.
  • Experience writing developer-friendly documentation, data dictionaries, architecture notes, and operational support guides.

What You'll Do:
  • Design, build, and optimize scalable data pipelines using strong data engineering fundamentals, including ETL/ELT design, data modeling, validation, monitoring, and performance-aware architecture.
  • Develop production-ready Python scripts, services, automation workflows, and data processing frameworks that are reliable, testable, maintainable, and well documented.
  • Use GitHub as a core engineering workflow, including version control, branching, pull requests, code reviews, release notes, deployment readiness, and collaborative development standards.
  • Create and maintain clear technical documentation for pipelines, APIs, MCPs, database objects, data models, deployment steps, dependencies, support procedures, and operational runbooks.
  • Where appropriate, use approved AI-assisted development workflows responsibly to support coding, debugging, testing, refactoring, documentation, and codebase understanding while maintaining human review, secure development practices, and sound engineering judgment.
  • Integrate data from internal systems, third-party platforms, APIs, and external partners while ensuring consistency, accessibility, data quality, and fit-for-purpose structures.
  • Administer and optimize relational databases and cloud data services, including Azure SQL Managed Instance, SQL Server, Snowflake or similar platforms, with responsibility for schema design, tuning, troubleshooting, and lifecycle management.
  • Develop governed data warehouse, data mart, and semantic-layer assets that support trusted reporting, operational analytics, executive decision-making, and scalable Business Intelligence delivery.
  • Build AI-ready data infrastructure, including MCP patterns, governed access points, reusable data services, and structured context layers that allow internal tools and agents to interact with enterprise data safely and effectively.
  • Prepare well-modeled datasets for Power BI, D3, React-based front ends, and other application or visualization experiences so users can access clear, accurate, and actionable insights.
  • Partner with Business Intelligence, Ticketing, Information Technology, Partnerships, Premium Services, Finance, and other stakeholders to translate business needs into durable technical solutions.
  • Continuously improve integration, transformation, deployment, documentation, and data preparation processes so SS&E can deliver faster reporting, stronger automation, and future-ready digital experiences.

Physical Requirements
  • Ability to work at a computer for extended periods of time.
  • Ability to participate in meetings, presentations, and collaborative work sessions.
  • Ability to work in an office environment with occasional on-site collaboration.
  • Ability to travel locally for meetings or organizational events as needed.

In every position, each employee is expected to: demonstrate alignment with SS&E's core values and mission, collaborate with internal/external community members and demonstrate ongoing development.
If you don't have experience in every single bullet above, no sweat - we still want to hear from you and encourage you to apply!
SS&E is an Equal Opportunity Employer
Nothing contained in this job description is intended to be a contract of employment, nor does any information contained here represent a guarantee of employment for a specific duration. Your employment with SS&E is "at will", which means that either you or SS&E may terminate the relationship at any time. Essential functions listed above must be performed with or without accommodations.

About AEG

Sourced by ZipRecruiter

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

Saint Louis, MO, US

Year founded

1992

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