Director, Enterprise Data Engineering & Analytics
At a glance
Director-level Data Engineering role at Mmc. New York · full-time · $139,000–$277,900 base.
dataroles summary, based on the employer's posting.
What you'll do
- Set the enterprise data direction and roadmap, matching platform investment to business needs and upcoming AI work
- Build and guide global data engineering, analytics engineering and data science teams, including hiring and mentoring
- Deliver reusable, trusted data products for reporting, analytics, machine learning and generative AI
- Review platform architecture, improve critical components, address production problems and establish engineering practices
- Work with business, consulting, technology and AI leaders to shape plans and steer platform and vendor choices
What you bring
- A computer science, engineering, data science or statistics degree, or equivalent practical experience
- At least ten years building data platforms or large-scale systems, including five years leading and growing engineering teams
- Hands-on Databricks and AWS expertise, with a record of delivering enterprise cloud data platform transformations
- Strong Python and SQL skills, plus experience with Spark, data modeling, ETL/ELT, CI/CD, observability and governance tools
- Experience designing AI-ready data architectures and applying security, privacy and regulatory requirements
Who this fits
This director-level role suits someone who combines technical leadership with team management and can still review architecture and resolve production issues. The role is based in New York and follows a hybrid schedule, with at least three days each week in the local office or at client sites.
From the employer
Company:
Oliver WymanDescription:
Oliver Wyman Technology is seeking an experienced Director of Enterprise 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.
We will count on you to:
Define and evolve the enterprise data strategy and roadmap, aligning investments with business priorities and future AI capabilities.
Lead, hire, and mentor globally distributed data engineering, analytics engineering, and data science teams while fostering a high-performing, inclusive culture.
Drive the design and delivery of trusted, reusable data products that power analytics, reporting, machine learning, and generative AI across the firm.
Provide hands-on technical leadership by reviewing architecture, optimizing key components, resolving production issues, and setting engineering standards and delivery practices.
Partner with senior business, consulting, technology, and AI platform leaders to shape the data roadmap, influence investment decisions, and manage vendor and platform priorities.
What you need to have:
Degree in Computer Science, Engineering, Data Science, Statistics, or equivalent practical experience.
10+ years designing and delivering data platforms or large-scale data systems, including 5+ years leading and scaling high-performing engineering teams.
Proven experience delivering enterprise cloud data platform transformations, with strong hands-on expertise in Databricks and AWS.
Strong software and data engineering skills, including Python and SQL, plus experience with Spark, data modeling, ETL/ELT, CI/CD, observability, and governance tooling.
Experience designing AI-ready data architectures that support machine learning and generative AI use cases, with strong knowledge of security, privacy, and regulatory requirements.
What makes you stand out:
Consulting or client-facing delivery experience in a complex enterprise environment.
Experience with streaming platforms and real-time architectures such as Kafka, Pub/Sub, or Kinesis.
Familiarity with vector-enabled data platforms, retrieval-optimized data models, and other modern Generative AI data patterns.
Exposure to additional cloud platforms such as Azure or GCP, or hybrid cloud environments.
Professional certifications such as Databricks or AWS Solutions Architect/Specialty.
Why join our team:
We help you be your best through professional development opportunities, interesting work and supportive leaders.
We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.
Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.