Head of Data Engineering, Corporate & Enterprise

About this role

Position Overview:  

The Head of Data Engineering, Corp & Enterprise is a   mid- leadership role accountable for the end-to-end data engineering lifecycle across Guardian's Corporate and business unit   and Enterprise,   from source data acquisition, ingestion, and transformation through the delivery of trusted, production-ready data products that power AI, machine learning, reporting, analytics, and business applications. This leader owns the strategy, architecture direction, engineering execution, and operational health of the data supply chain that downstream teams depend on every day.  

This is a player-coach role. The Head of Data Engineering leads a team of   US   FTEs and provides direction to an extended indirect engineering team in India, building a high-performing, globally distributed delivery model with clear standards, strong engineering discipline, and a culture of ownership. While the role is primarily a leadership position, the leader is expected to stay technically current and go hands-on when needed   reviewing   designs,   unblocking   complex engineering problems, and setting the bar for quality through example.  

A core mandate of this role is ownership and   maturing   Guardian's Master Data Management (MDM) practice. The leader will manage MDM operations and evolve the practice from foundational capability to enterprise-grade discipline   -   strengthening match and merge rules, survivorship, golden record quality, data stewardship workflows, and domain expansion. Deep, practical knowledge of Reltio is essential, including its data model, match engine, workflows, integrations, and APIs.  

Equally important, this leader carries a transformation mandate: to take data engineering to its next level using AI agents and agentic AI. This means reimagining how pipelines are built, tested,   monitored , and healed :   embedding AI-assisted development, autonomous data quality and observability agents, and agentic workflows across the engineering lifecycle to dramatically increase speed, reliability, and scale. The right candidate sees agentic AI not as a tool to adopt, but as a new operating model for data   engineering and   has the vision and pragmatism to lead the organization   and the new operating model .  

Key Responsibilities:  

• Own end-to-end data engineering for the   Corporate   business units   and Enterprise   -   source data acquisition, ingestion, integration, transformation, curation, and publication of production-ready data products for AI, ML, reporting, analytics, and application consumption  

• Define and execute the data engineering strategy and multi-year roadmap, aligned to business unit priorities and Guardian's enterprise data and AI strategy  

• Lead, coach, and develop a team of FTE data engineers; provide direction, standards, and delivery oversight to an indirect engineering team in India,   operating   a seamless follow-the-sun global delivery model  

• Operate as a player-coach ,   remain hands-on as needed with solution design, code and pipeline reviews, complex troubleshooting, and critical delivery moments  

• Own and manage the MDM platform and operations on Reltio, including data modeling, match/merge rules, survivorship, workflows, integrations, and API-based consumption  

• Mature the MDM practice into an enterprise-grade   discipline ,    improve   golden record quality, expand mastered domains, strengthen data stewardship and governance workflows, and measure and communicate MDM business value  

• Lead the transformation of data engineering using agentic AI ,   deploy AI agents for pipeline development, code generation, testing, documentation, data quality monitoring, anomaly detection, and self-healing operations  

• Champion AI-assisted engineering practices across the team, redefining team workflows, productivity expectations, and engineering roles for an agentic future  

• Deliver governed, discoverable, reusable data products with clear contracts, SLAs, lineage, and ownership ,   treating data as a product with defined consumers and measurable quality  

• Partner closely with AI/ML, analytics, reporting, and application teams to ensure data products are AI- ready ,    supporting   feature pipelines, model training data, and retrieval-ready datasets for GenAI use cases  

• Establish and enforce engineering standards, reusable patterns, CI/CD,   DataOps   automation, and observability across all pipelines and platforms  

• Build reusable tools, libraries, frameworks, and shared services that productize and scale engineering best practices, and actively drive their adoption across Data Engineering to accelerate delivery, raise quality, and   eliminate   duplicated effort  

• Drive data quality, reliability, and operational excellence   -   define SLAs/SLOs, reduce incidents, and ensure resilient, cost-optimized cloud data platform operations  

• Embed data governance, privacy, security, and regulatory compliance requirements into all data engineering and MDM solutions in partnership with Data Governance, Cybersecurity, and Risk  

• Manage vendor relationships and platform economics, including Reltio and cloud data platform spend, with a focus on value and FinOps discipline  

• Influence and align senior business and technology stakeholders; translate business needs into engineering priorities and communicate progress, risks, and outcomes with clarity  

• Recruit,   retain , and grow top engineering talent; build succession depth and elevate the technical maturity of both onshore and offshore teams  

• Available for production incidents with major impact to business operations  

You Have:  

• 12+ years of progressive data engineering experience, including 5+ years leading data engineering teams and delivery at scale in a large enterprise   and have experience in Finance, actuaries, HR related   data   and workflows.   

• Proven experience leading globally distributed teams, including dire

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