Global Head of Data Ingestion

Marsh McLennan • New York, NY

1d • Hybrid

About The Position

We are seeking a Global Head of Data Ingestion to join our Chief Innovation & Data Office team at Marsh. This role will be based in New York, Boston, or London. This is a hybrid role that has a requirement of working at least three days a week in the office. The Global Head of Data Ingestion is a senior leadership role within the Chief Innovation & Data Office, responsible for designing, executing, and continuously improving the enterprise strategy for acquiring, validating, and delivering high-quality data at scale. This leader will define how data, both structured and unstructured and from internal systems and external partners alike, flows into a golden-source data environment ready for analytics, AI, and commercial use, as well as into downstream systems. The role carries both strategic authority over the ingestion architecture and operational accountability for cost, speed, and accuracy. The position encompasses three interrelated mandates. First, helping craft the domain-led strategy for what data to ingest and in what priority order, working closely with business leaders to connect those efforts investments to commercial value. Second, owning the technical architecture and tooling that makes ingestion fast, repeatable, and scalable; that includes oversight of engineering teams and vendor partnerships that build and maintain bespoke ingestion products. Third, leading a centralized human-in-the-loop Capability Center that provides final data validation where automated methods alone are not yet sufficient, with an explicit mandate to shrink that manual footprint over time as models and processes mature. This is neither a pure strategy role, nor a pure operations role. We are looking for someone who can articulate a compelling vision for enterprise data ingestion, hold genuine technical authority over how tooling is built inside of our enterprise architectures, and deliver hard financial results through consolidation, automation, and industrialization. The successful candidate will have a builder’s instinct, a strategist’s perspective, and the pragmatism to know that some of the most valuable data in insurance still requires a human to verify it.

Requirements

• 15+ years of experience in data engineering, data management, or data architecture, with at least 5 years in a senior leadership role with global scope.
• Demonstrated track record building and operating large-scale, enterprise-grade data ingestion capabilities across multiple geographies, data domains, and cloud environments.
• Strong understanding of application architecture and modern data platforms, including experience with technologies such as Spark, Databricks, Airflow, and cloud-native data services (AWS, Azure, GCP).
• Fluency in how AI and large language models can be applied to ingestion challenges: document extraction, content classification, schema mapping, and code generation for data transformation.
• Strong grasp of data governance, data quality, master data management, and metadata/lineage frameworks, with practical implementation experience.
• Proven ability to deliver structural cost savings through platform consolidation, vendor rationalization, and operational industrialization.
• Experience managing significant platform budgets and vendor relationships with financial discipline.
• Familiarity with regulatory and compliance frameworks governing data usage (GDPR, SOX, and equivalent).
• Excellent stakeholder management and communication skills: able to align executive sponsors, technology teams, and business users around a shared ingestion strategy.

Responsibilities

• Domain-Led Ingestion Strategy: Define and maintain a prioritized, domain-based ingestion strategy that ties every ingestion workstream to a specific commercial or operational outcome. Partner with business leaders across the enterprise to identify which data elements and sources drive the highest-value outcomes and prioritize their ingestion over lower-value noise. Break down line-of-business silos to ensure data is ingested in a way that supports a unified client view across the enterprise, rather than fragmented, business-unit-specific narratives. Navigate the diversity of ingestion challenges across the enterprise: from extracting structured information out of PDF documents and emails, to remapping and harmonizing data that arrives structured but in incompatible formats from HR systems, claims platforms, or carrier feeds.
• Ingestion Architecture & Technical Authority: Own the target-state ingestion architecture: define the standards, patterns, and guardrails for how data enters the enterprise data environment (Databricks/MIDAS), whether through automated pipelines, API-based integrations, or human-assisted workflows. Where appropriate, define how ingested data is piped directly to downstream operational systems. Drive modernization from bespoke, fragile integration points toward scalable, repeatable ingestion patterns that reduce the marginal cost of onboarding new data domains. Bring deep understanding of how large language models and AI can be applied to ingestion problems: extracting structured fields from unstructured documents, classifying and routing content, and generating deterministic code to automate schema mapping and data transformation between systems. Maintain hands-on technical credibility: you will not write production code, but you must be able to design solutions, evaluate engineering proposals on their merits, and hold technology partners to a high bar on architectural quality. Refuse short-term point solutions that create long-term complexity. Enforce the discipline that automation layered on top of broken processes or inconsistent standards only increases cost.
• Ingestion Product Development & Vendor Management: Oversee the development and continuous improvement of internal ingestion products and tools, including document ingestion engines, schema mapping utilities, API connectors, and data capture interfaces, and manage the engineering teams and technical leaders who build them. Evaluate, select, and manage third-party vendor solutions where containerized or off-the-shelf products can accelerate ingestion goals; negotiate and hold vendors to performance and cost commitments while building toward reduced long-term reliance on external providers. Liaise with internal technology partners when building new capabilities or experimenting with emerging approaches, ensuring alignment between innovation efforts and enterprise architectural standards. Rationalize the existing landscape of ingestion tools and platforms across regions and business lines, eliminating redundancy and reducing vendor sprawl.
• Human-in-the-Loop Capability Center: Hold executive accountability for a centralized Capability Center (approximately 250 colleagues across India, Warsaw, KL, and potentially other locations) responsible for final data validation, human-assisted processing, and, in today’s operating model, direct data entry into source systems where automated ingestion is not yet in place. Ensure the Capability Center operates against clear SLAs for speed, accuracy, and cost per record or document, functioning as a service bureau to the business: inputs in, validated data out. Own the “Shrink to Scale” mandate: continuously challenge the size of the manual operation by deploying AI-assisted tooling and improved ingestion technology to automate tasks that are currently performed by hand, reducing headcount over time even as data volume and scope grow. Invest in the tooling and user experience for human validators: world-class, AI-augmented interfaces that make verification fast, error-proof, and directly integrated with the golden data layer, replacing manual rekeying with confirmation-based workflows wherever possible. Appoint and work through a dedicated Capability Center leader for day-to-day operations, freeing this role to focus on broader strategy, architecture, and commercial outcomes.
• Cost Accountability & Industrialization: Deliver substantial, sustained run-rate cost reductions through consolidation of fragmented data teams, replacement of manual effort and vendor spend with automated ingestion, and elimination of duplicative pipelines and rekeying. Establish and track unit economics for ingestion: cost per data domain onboarded, cost per record validated, and platform productivity per dollar invested. Manage significant platform and operational budgets with financial discipline, demonstrating measurable ROI on ingestion investments. Drive structural improvements, not temporary savings, that are durable, repeatable, and scale as the enterprise takes on additional data domains and use cases.
• Enablement of Analytics & AI: Ensure that all ingestion pathways, both automated and human-assisted, result in clean, validated, well-cataloged data entering the golden-source environment, ready for downstream analytics and AI consumption. Standardize repeatable data preparation and integration patterns that reduce friction for data science, analytics, and AI engineering teams seeking to build on ingested data. Collaborate with governance and platform teams to ensure metadata, lineage, and quality frameworks are embedded in ingestion processes by design, not retrofitted after the fact. Reduce the marginal cost of enabling new analytics and AI use cases through reusable, governed ingestion assets.

Benefits

• In addition to the base salary, this position may be eligible for performance-based incentives.
• We are excited to offer a competitive total rewards package which includes health and welfare benefits, tuition assistance, 401K savings and other retirement programs as well as employee assistance programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Executive
• Education Level: No Education Listed