Data Engineer

San Antonio Spurs Sports & Entertainment•San Antonio, TX

3d•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.