Sr. Director of Data Engineering

Orangepeople

Job Location:

Inglewood, CA - USA

Monthly Salary: Not Disclosed

Posted on: 4 hours ago

Vacancies: 1 Vacancy

Job Summary

OP is partnering with one of the premier professional sports and entertainment organizations that has built one of the most seamless fan-first experiences in live sports through a state-of-the-art arena environment. The focus of this role is to enhance a data-driven automated home-court advantage for both the team and business operations while maintaining an authentic and engaging fan experience.

This is a full-time opportunity based in Southern California offering a comprehensive benefits package including medical dental vision 401(k) with company contribution wellness allowances and additional employee perks. Due to the dynamic nature of live sports and entertainment events flexibility to work evenings weekends holidays and major event schedules is essential.

Role Summary:

The Director of Data Engineering Platform & Governance will architect build and operate the organizations next-generation data platform powering fan engagement business operations and AI innovation across the Intuit Dome ecosystem. This role is responsible for designing a secure real-time analytics- and AI-ready and unified data platform that transforms fan interaction data including mobile app events ticketing marketing concessions computer vision and biometric signals into trusted cataloged governed datasets that power marketing sales operations and AI applications.

Reporting to the Chief Data Analytics & AI Officer this role combines hands-on data platform leadership with enterprise data and AI governance responsibilities. During the initial phase the Director will work with a small engineering team and will be expected to contribute directly to platform architecture pipeline development and governance implementation.

Key Responsibilities:

Data Platform Architecture & Engineering.
- Define and deliver the modern data and AI platform enabling real-time fan intelligence and AI.
- Architect and implement the end-to-end data platform including edge ingestion streaming pipelines batch processing and curated analytics and MLOps layers in collaboration with the infrastructure team.
- Build and maintain scalable low-latency data pipelines integrating ticketing CRM MarTech concessions IoT and fan experience systems.
- Design analytics-ready data models and marketing schemas that power segmentation campaign orchestration and revenue optimization.
- Develop real-time data services and APIs supporting fan engagement intelligence applications and AI products.
- Establish engineering standards for data quality observability reliability governance security and cost optimization in alignment with existing infrastructure and cyber standards and policies.
- Ensure the platform supports advanced analytics machine learning and AI product development in collaboration with the infrastructure team.
Data Engineering Leadership.
- Lead the design and execution of the organizations data engineering strategy.
- Define and execute the data platform roadmap aligned with fan intelligence and AI initiatives.
- Provide technical leadership across data engineering and MLOps environments.
- Partner closely with AI analytics marketing sales product security and technology teams.
- Influence vendor selection tooling strategy and platform architecture decisions.
- Drive performance scalability and cost optimization across the data ecosystem.
- Act as a hands-on technical leader contributing to architecture design and engineering implementation.
Data Governance & Data Trust.
- Establish enterprise-grade governance ensuring data is secure trusted and usable at scale.
- Define and enforce data governance policies and standards including quality privacy security and retention
- Establish data ownership stewardship and enterprise data definitions.
- Implement frameworks for data quality monitoring lineage tracking and metadata management.
- Define controls for data access sharing and usage across internal teams and external partners in collaboration with the infrastructure team.
- Partner with Legal and Security to ensure compliance with CCPA GDPR emerging AI regulations and internal policies.
AI Governance & Responsible AI.
- Establish the governance frameworks ensuring AI systems are safe compliant and trustworthy.
- Operational lead for the AI Governance Council establishing governance frameworks and decision processes. Classify AI. systems by risk level and regulatory impact and define approved vs restricted use cases.
- Define standards for AI model lifecycle governance including approval monitoring and auditability.
- Implement controls for model monitoring drift detection bias mitigation and human-in-the-loop oversight.
- Establish guidelines for AI explainability transparency and customer impact review.
- Ensure AI models are trained and deployed using approved high-quality data sources and meet governance standards.

Qualifications:

Required Experience:
- 9 years of experience in data engineering data platform architecture or data infrastructure.
- Proven experience designing and operating modern data platforms at scale.
- Strong experience with real-time streaming batch processing data lakehouse architectures and analytic data business logic.
- Hands-on experience building data pipelines and distributed data systems.
- Experience supporting AI/ML environments and MLOps pipelines.
- Experience supporting MarTech stacks.
- Experience implementing data governance data quality and privacy frameworks.
- Ability to operate as a hands-on technical leader in a lean engineering environment.
Technical Expertise:
- Strong experience with many of the following:
- Cloud platforms (Azure AWS GCP).
- Data lakehouse technologies (Databricks Azure Fabric BigQuery Snowflake etc.).
- Streaming platforms (Kafka Kinesis Pub/Sub or equivalent).
- Workflow orchestration (Airflow Dagster Prefect).
- Data transformation frameworks (dbt or equivalent).
- Data observability and monitoring tools.
- API architecture and data services.
- CI/CD and infrastructure-as-code practices.
Governance & Leadership Experience:
- Experience implementing data governance or data management frameworks.
- Familiarity with AI governance model lifecycle management and responsible AI practices.
- Experience working with privacy regulations (CCPA GDPR).
- Ability to collaborate with legal IT/security marketing product and engineering stakeholders.

Role Summary:

Key Responsibilities:

Data Platform Architecture & Engineering.
- Define and deliver the modern data and AI platform enabling real-time fan intelligence and AI.
- Architect and implement the end-to-end data platform including edge ingestion streaming pipelines batch processing and curated analytics and MLOps layers in collaboration with the infrastructure team.
- Build and maintain scalable low-latency data pipelines integrating ticketing CRM MarTech concessions IoT and fan experience systems.
- Design analytics-ready data models and marketing schemas that power segmentation campaign orchestration and revenue optimization.
- Develop real-time data services and APIs supporting fan engagement intelligence applications and AI products.
- Establish engineering standards for data quality observability reliability governance security and cost optimization in alignment with existing infrastructure and cyber standards and policies.
- Ensure the platform supports advanced analytics machine learning and AI product development in collaboration with the infrastructure team.
Data Engineering Leadership.
- Lead the design and execution of the organizations data engineering strategy.
- Define and execute the data platform roadmap aligned with fan intelligence and AI initiatives.
- Provide technical leadership across data engineering and MLOps environments.
- Partner closely with AI analytics marketing sales product security and technology teams.
- Influence vendor selection tooling strategy and platform architecture decisions.
- Drive performance scalability and cost optimization across the data ecosystem.
- Act as a hands-on technical leader contributing to architecture design and engineering implementation.
Data Governance & Data Trust.
- Establish enterprise-grade governance ensuring data is secure trusted and usable at scale.
- Define and enforce data governance policies and standards including quality privacy security and retention
- Establish data ownership stewardship and enterprise data definitions.
- Implement frameworks for data quality monitoring lineage tracking and metadata management.
- Define controls for data access sharing and usage across internal teams and external partners in collaboration with the infrastructure team.
- Partner with Legal and Security to ensure compliance with CCPA GDPR emerging AI regulations and internal policies.
AI Governance & Responsible AI.
- Establish the governance frameworks ensuring AI systems are safe compliant and trustworthy.
- Operational lead for the AI Governance Council establishing governance frameworks and decision processes. Classify AI. systems by risk level and regulatory impact and define approved vs restricted use cases.
- Define standards for AI model lifecycle governance including approval monitoring and auditability.
- Implement controls for model monitoring drift detection bias mitigation and human-in-the-loop oversight.
- Establish guidelines for AI explainability transparency and customer impact review.
- Ensure AI models are trained and deployed using approved high-quality data sources and meet governance standards.

Qualifications:

Required Experience:
- 9 years of experience in data engineering data platform architecture or data infrastructure.
- Proven experience designing and operating modern data platforms at scale.
- Strong experience with real-time streaming batch processing data lakehouse architectures and analytic data business logic.
- Hands-on experience building data pipelines and distributed data systems.
- Experience supporting AI/ML environments and MLOps pipelines.
- Experience supporting MarTech stacks.
- Experience implementing data governance data quality and privacy frameworks.
- Ability to operate as a hands-on technical leader in a lean engineering environment.
Technical Expertise:
- Strong experience with many of the following:
- Cloud platforms (Azure AWS GCP).
- Data lakehouse technologies (Databricks Azure Fabric BigQuery Snowflake etc.).
- Streaming platforms (Kafka Kinesis Pub/Sub or equivalent).
- Workflow orchestration (Airflow Dagster Prefect).
- Data transformation frameworks (dbt or equivalent).
- Data observability and monitoring tools.
- API architecture and data services.
- CI/CD and infrastructure-as-code practices.
Governance & Leadership Experience:
- Experience implementing data governance or data management frameworks.
- Familiarity with AI governance model lifecycle management and responsible AI practices.
- Experience working with privacy regulations (CCPA GDPR).
- Ability to collaborate with legal IT/security marketing product and engineering stakeholders.