Head of Data Platform Engineering

Who We Are

At Corebridge Financial, we believe action is everything. That’s why every day we partner with financial professionals and institutions to make it possible for more people to take action in their financial lives, for today and tomorrow.

• We are stronger as one :
  We collaborate across the enterprise, scale what works and act decisively for our customers and partners.
• We deliver on commitments :
  We are accountable, empower each other and go above and beyond for our stakeholders.
• We learn, improve and innovate :
  We get better each day by challenging the status quo and equipping ourselves for the future.
• We are inclusive :
  We embrace different perspectives, enabling our colleagues to make an impact and bring their whole selves to work.

The Information Technology organization is the technological foundation of our business and works in collaboration with our partners from across the company. The team drives technology and digital transformation, partners with business leaders to design and execute new strategies through IT and operations services and ensures the necessary IT risk management and security measures are in place and aligned with enterprise architecture standards and principles.

The Head of Data Platform Engineering will be responsible for leading the strategy, design, implementation, and governance of the enterprise data platform for a leading Life & Annuities Insurance company. This senior leader will drive the modernization of the data landscape, enabling cloud‑native big data solutions, ensuring secure and scalable data migrations, and supporting AI / ML initiatives via robust MLOps / Model Ops practices.

The ideal candidate is a seasoned technologist and visionary with deep expertise in big data platforms, cloud architectures, and enterprise‑scale data transformations, coupled with strong leadership acumen to manage diverse teams of data engineers, analysts, data scientists, data stewards and MLOps professionals.

• Strategic Leadership :
  Define and lead the long‑term data platform engineering roadmap aligned with business and digital transformation goals.
• Cloud Data Architecture :
  Architect and deliver cloud‑native data solutions (e.g., AWS, Azure, GCP) including data lakes, cloud data warehouses (example Snowflake), ETL frameworks, and real‑time streaming pipelines.
• Platform Engineering :
  Build and optimize scalable and secure data platforms that support high‑volume processing, analytics, and machine learning workloads.
• Data Migration & Modernization :
  Lead complex migrations from legacy systems to modern cloud platforms, ensuring minimal disruption and compliance with data governance policies.
• Data Management & Quality :
  Oversee Master Data Management (MDM), and partner with Data Governance and Data Security group on metadata management, data cataloging, profiling, and lineage.
• MLOps / Model Ops Engineering Support :
  Enable scalable and reliable deployment and monitoring of ML models across platforms; integrate model governance into data pipelines.
• Technology Evaluation & Selection :
  Evaluate emerging technologies and tools to ensure the platform remains cutting‑edge and aligned with business priorities.
• Collaboration & Stakeholder Engagement :
  Partner with business, IT, and analytics teams to ensure data solutions meet enterprise needs.
• Team Leadership :
  Manage and grow a high‑performing global team of data engineers, analysts, and MLOps engineers. Foster a culture of innovation, excellence, and continuous learning.
• Working with the L&R IT leadership team to embrace contemporary ways of working, attract and develop talent across the firm and leverage economies of scale and shared capabilities.

• Business / Industry Knowledge :
  Demonstrates an understanding of the insurance, financial services industry, especially the life and annuities space and wealth management segments.
• Technical Acumen :
  Proven experience with cloud-native data platforms (AWS, Azure, or GCP). Expertise in big data technologies (e.g., Hadoop, Spark, Kafka, Databricks). Hands‑on experience with ETL / ELT frameworks (e.g., Apache NiFi,…