Head of Data Engineering, Corporate & Enterprise

Guardian Life Insurance•Holmdel Township, NJ

3d•$152,290 - $250,195•Hybrid

About The Position

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.

Requirements

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 direct FTE leadership and indirect/offshore teams in India, within a matrixed organization
Demonstrated ability to operate as a player-coach - setting strategy and leading people while staying hands-on with architecture, design, and engineering when needed
Deep expertise across the end-to-end data engineering lifecycle: data acquisition, batch and streaming ingestion, ELT/ETL, data modeling, curation, and productionization of data products
Strong hands-on knowledge of the modern data stack including cloud data platforms (e.g., Fivetran, Databricks), orchestration (e.g., Databricks, Airflow), transformation frameworks (e.g., Spark, dbt), streaming (e.g., Kafka), and CI/CD-driven DataOps
A strong software engineering mindset and execution skills, with a proven track record of designing and building reusable tools, libraries, and frameworks, applying sound software design principles, testing, and documentation, and driving their adoption across engineering teams to multiply productivity and impact
Strong Master Data Management expertise with deep, practical knowledge of Reltio - data modeling, match/merge and survivorship configuration, workflows, integrations, and APIs and a track record of maturing an MDM practice
Forward-leaning experience with AI-assisted and agentic engineering using LLMs and AI agents for code generation, testing, data quality, observability, and autonomous operations and a compelling vision for agent-driven data engineering
Experience delivering AI/ML-ready data and GenAI/retrieval-ready data foundations
Strong grounding in data governance, data quality, metadata, lineage, and privacy/security practices, ideally in a regulated industry such as insurance or financial services
A data-as-a-product mindset with experience defining data contracts, SLAs, and consumer-driven design
Excellent leadership and communication skills, with the ability to influence senior stakeholders and convey complex technical concepts to business audiences
Experience with Agile / product-based delivery models and managing delivery across onshore/offshore teams
A bias for action, high standards, and the ability to balance transformation ambition with operational stability

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