Director - Data Engineering

Director, Enterprise Data Engineering & Analytics Oliver Wyman Technology is seeking an experienced Director of Entperise Data Engineering & Analytics to lead and scale our enterprise data platform and analytics capabilities. This strategic leadership role is responsible for building trusted, governed and scalable data foundations that power analytics, reporting and AI across the firm. Working closely with AI platform, application and business teams, the Director will ensure enterprise data is discoverable, high quality and AI-ready, enabling the successful adoption of machine learning and generative AI capabilities. The role combines strategic leadership with hands-on technical expertise, leading multidisciplinary teams while delivering modern cloud data platforms and analytics products that create measurable business value. The ideal candidate has deep, practical experience with Databricks and AWS and a proven track record of delivering enterprise data platforms that enable analytics, machine learning and generative AI at scale.

Responsibilities

• Define and evolve the enterprise data strategy and roadmap, aligning investments with business priorities and future AI capabilities.
• Lead, hire and mentor globally distributed teams of data engineers, analytics engineers and data scientists, setting priorities and delivery cadence while fostering a high-performing, inclusive engineering culture.
• Lead the design and delivery of trusted, reusable data products that enable analytics, reporting and AI capabilities across the firm.
• Provide hands‑on technical leadership: design and review architecture, implement or optimise key components, and resolve production incidents as required.
• Establish engineering standards, architectural principles and delivery practices that improve the quality, scalability and resilience of enterprise data solutions.
• Enable self-service analytics through well-governed semantic models, reusable datasets and curated enterprise data assets.
• Partner with AI platform, engineering, consulting and product teams to deliver AI-ready data products by ensuring data pipelines, metadata, lineage and governance meet the needs of machine learning and generative AI use cases
• Develop and maintain AI-ready data architecture, ensuring enterprise data is structured, governed and accessible for analytics, machine learning and generative AI applications.
• Define and enforce data governance, metadata, lineage, security and data quality standards that enable trusted analytics and responsible AI adoption.
• Build trusted relationships with senior business, consulting and technology leaders to shape the data roadmap, influence investment decisions and prioritize delivery against business outcomes.
• Manage vendor relationships and platform budgets; evaluate and procure third‑party tools where appropriate.
• Foster a culture of data literacy, experimentation, inclusivity and continuous improvement.

Must Have Skills And Qualifications

• Degree in Computer Science, Engineering, Data Science, Statistics or equivalent practical experience.
• 10+ years designing and delivering data platforms or large‑scale data systems; 5+ years experience building, mentoring and scaling high-performing engineering teams, developing technical leaders and fostering an inclusive engineering culture.
• Proven experience delivering end‑to‑end cloud data platform transformations at enterprise scale.
• Hands‑on Databricks experience (Spark optimisation, Delta Lake, workspace/job orchestration, Unity Catalog) at scale.
• Strong practical experience building and operating data solutions on AWS (e.g., S3, Glue, Redshift/Athena, Lambda, EKS/ECS; infrastructure as code such as CloudFormation or Terraform).
• Experience designing and delivering data platforms that support machine learning and generative AI use cases, including an understanding of AI-ready data architectures, metadata, governance and data quality requirements.
• Strong understanding of modern data architecture patterns including lakehouse architectures, data products, semantic models and API-driven integration.
• Strong software and data engineering skills: Python and SQL required; Scala/Java advantageous. Experience with Spark, data modelling, ETL/ELT and streaming fundamentals.
• Experience implementing CI/CD, container orchestration and observability for data systems.
• Knowledge of data governance, metadata/catalogue tools, lineage and data quality frameworks (i.e. Great Expectations or equivalent).
• Strong grasp of security, data privacy and regulatory requirements (e.g., GDPR, data residency).
• Professional certification such as Databricks or AWS (Solutions Architect / Specialty).

Nice to have

• Consulting or client‑facing delivery experience.
• Experience with streaming platforms (Kafka, Pub/Sub, Kinesis) and real‑time architectures.
• Experience supporting Generative AI initiatives through modern data architectures, including vector-enabled data platforms, metadata management and retrieval-optimized data models.
• Exposure to other cloud providers (Azure/GCP) or hybrid cloud architectures. Marsh (NYSE: MRSH) is a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information, visit corporate.marsh.com, or follow us on LinkedIn and X. Marsh is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, caste, disability, ethnic origin, family duties, gender orientation or expression, gender reassignment, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law. Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.

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