Data Operations Engineer

Orlando Magic • Orlando, FL

3h • Hybrid

About The Position

The Data Engineering & Cloud Architecture group owns and operates the organization’s data warehouse, business and basketball analytics platforms, and CRM ecosystem to drive performance, innovation, and long-term growth. The Data Operations Engineer is responsible for the reliability, scalability, and operational excellence of the organization’s data platforms and analytics products. This role sits at the intersection of data engineering and analytics, ensuring that data pipelines, dashboards, models, and AI-driven solutions are production-ready, well-governed, and continuously monitored. Partnering closely with BI engineering, analytics, and other business and basketball stakeholders, the Data Operations Engineer designs and maintains robust data workflows, implements data quality and observability frameworks, and manages deployment processes across modern cloud-based infrastructure. This role also supports API integrations, model deployment, and emerging capabilities such as embedded analytics, A.I. systems, and privacy-safe data collaboration environments. With a strong focus on automation, testing, documentation, and system optimization, the Data Operations Engineer drives continuous improvement in data platform performance, cost efficiency, and operational maturity. This position plays a key role in establishing best practices across data governance, security, and analytics operations, while supporting internal platforms and ensuring seamless delivery of trusted data to stakeholders across the organization.

Requirements

• 4-year degree in Computer Science, Data Analytics, Information Systems or equivalent work experience and three+ (3+) years supporting data production, analytics, or BI platforms in a data operations, data engineering, or analytics engineering role preferred.
• Strong analytical and problem‑solving skills, with the ability to translate technical issues into clear business impact required.
• Working knowledge of analytics engineering tools or patterns (e.g., dbt, semantic layers, metrics governance)
• Hands‑on experience using Python, or similar, to support data workflows, automation, validation, or operational tooling required.
• Strong working experience with Snowflake, including querying, performance optimization, and supporting analytics workloads preferred.
• Experience working in AWS environments, including familiarity with cloud storage, compute, and data integration patterns preferred.
• Demonstrated ability to monitor, troubleshoot, and resolve data quality, pipeline, or API issues in a production environment preferred.
• Experience collaborating with BI, analytics, and engineering teams on documentation, QA, and release processes preferred.
• Familiarity with data quality frameworks, monitoring dashboards, and alerting systems.
• Experience supporting or deploying machine learning models, AI tools, or embedded analytics in production environments.
• Exposure to data clean rooms, privacy‑safe data sharing, or governed external data collaborations.
• Comfort working in Agile or cross‑functional team environments, balancing multiple initiatives and operational priorities.
• Willingness to work a flexible schedule including nights and weekends and be on-call as necessary based on the changing priorities of the department.
• Proficient in all Microsoft Office products and other related computer skills required.
• Ability to meet tight deadlines and work well under pressure.
• Strong organizational skills, time management skills and attention to detail required.
• Strong verbal and written communication skills with an emphasis on business writing skills.
• Ability to prioritize and manage multiple tasks/projects.
• Ability to work independently without supervision, be self-directed and demonstrate initiative.
• Ability to work half Magic home games (20). This includes the ability to work a flexible schedule including nights and weekends and be on-call as necessary based on the changing priorities of the department.

Responsibilities

• Own and maintain data operations workflows, ensuring analytics products, dashboards, and models are reliable, performant, and production‑ready
• Develop and maintain data quality monitoring, dashboards, and alerting systems to proactively identify issues across core datasets and pipelines
• Design, deploy, and operate production-grade data services and pipelines, including CI/CD automation (e.g., GitHub Actions), containerization (Docker), and cloud orchestration (e.g., AWS ECS)
• Establish and maintain logging, monitoring, and observability standards for data systems (e.g., pipeline health, latency, and failures)
• Develop automated testing frameworks for data pipelines, including unit, integration, and regression testing
• Partner with BI Engineering and Analytics teams on documentation, QA processes, and release coordination for new and existing data products
• Support model deployment and AI implementations, including BI chatbots and embedded analytics, ensuring proper validation, monitoring, and operational handoff
• Manage and support API integrations, including troubleshooting data ingestion, delivery, and downstream consumer issues
• Assist in the implementation and ongoing operation of data clean rooms and privacy‑safe data collaboration initiatives
• Implement and maintain data access controls, secrets management, and secure data handling practices
• Optimize data warehouse performance, compute spend, and system utilization, providing actionable reporting and recommendations
• Own administration of Magic Insights and other automated reporting management, including configuration, monitoring, and stakeholder support
• Administer and support internal platforms such as the in‑house App Badge CMS, ensuring data accuracy, access controls, and system reliability
• Collaborate with cross‑functional partners to improve data governance, operational standards, and analytics best practices across the organization
• Create and maintain technical documentation for pipelines, systems, and operational processes
• Drive continuous improvement of data platform reliability and operational maturity
• All other duties as assigned.

Benefits

• 18 days of personal time off per year + 13 holidays (that is 31 paid days off year!) plus reduced summer work hours (every other Friday off during the off-season, which averages to another 8 days)
• A hybrid work model, casual work attire on non-game days, staff tickets to Magic home games, learning and development opportunities, Employee Resource Groups (ERGs), company sponsored events, volunteer opportunities & outings for every employee
• Fantastic benefits that include: medical, dental, vision, 401(k) with company matching, mental wellness resources, subsidized gym memberships, maternity & paternity leave
• Culture built on Community, Innovation, Legendary and Teamwork!

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree