Company:
Oliver Wyman
Description:
Job title Director Data Engineering & Analytics
Overview Oliver Wyman Technology is seeking an experienced handson Director of Data Engineering & Analytics to lead and scale our enterprise data platform and analytics capability. This role combines strategic ownership with technical leadership: you will grow and mentor a multidisciplinary team (data engineers analytics engineers and data scientists) and deliver productiongrade data platforms analytics products and AI/ML solutions that generate measurable business value for the firm and our clients. The ideal candidate has deep practical experience with Databricks and AWS and a proven track record of productionising data and ML systems at scale.
Responsibilities
- Lead hire and mentor a highperforming team; set priorities delivery cadence and engineering standards to scale the capability.
- Own the roadmap and delivery of enterprise data platforms and analytics products including data lakes/warehouses ETL/ELT streaming and data APIs.
- Provide handson technical leadership: design and review architecture implement or optimise key components and resolve production incidents as required.
- Drive adoption of Databricks best practice (Spark optimisation Delta Lake Unity Catalog) and core cloud platform patterns.
- Productionise AI/ML: guide feature engineering model deployment monitoring versioning explainability and MLOps workflows.
- Establish and enforce CI/CD automated testing observability and incident response for data pipelines models and analytics services.
- Define and enforce data governance security privacy and compliance standards in partnership with legal and security teams.
- Partner with business leaders product managers and consulting teams to translate business problems into scalable data and AI solutions; manage stakeholder expectations and prioritisation.
- Manage vendor relationships and platform budgets; evaluate and procure thirdparty 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 largescale data systems; 5 years managing engineering teams and senior technical leaders.
- Proven experience delivering endtoend cloud data platform transformations at enterprise scale.
- Handson 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).
- Solid understanding of AI/ML production patterns including model development deployment monitoring drift detection and MLOps.
- 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 clientfacing delivery experience.
- Experience with streaming platforms (Kafka Pub/Sub Kinesis) and realtime architectures.
- Familiarity with generative AI LLMs and conversational AI production patterns.
- Exposure to other cloud providers (Azure/GCP) or hybrid cloud architectures.
Marsh McLennan (NYSE: MMC) is a global leader in risk strategy and people advising clients in 130 countries across four businesses: Marsh Guy Carpenter Mercer and Oliver Wyman. With annual revenue of $24 billion and more than 90000 colleagues Marsh McLennan helps build the confidence to thrive through the power of perspective. For more information visit or follow on LinkedIn and X.
Marsh McLennan 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 McLennan 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 McLennan 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.
Required Experience:
Director
Company:Oliver WymanDescription:Job title Director Data Engineering & AnalyticsOverview Oliver Wyman Technology is seeking an experienced handson Director of Data Engineering & Analytics to lead and scale our enterprise data platform and analytics capability. This role combines strategic ownership w...
Company:
Oliver Wyman
Description:
Job title Director Data Engineering & Analytics
Overview Oliver Wyman Technology is seeking an experienced handson Director of Data Engineering & Analytics to lead and scale our enterprise data platform and analytics capability. This role combines strategic ownership with technical leadership: you will grow and mentor a multidisciplinary team (data engineers analytics engineers and data scientists) and deliver productiongrade data platforms analytics products and AI/ML solutions that generate measurable business value for the firm and our clients. The ideal candidate has deep practical experience with Databricks and AWS and a proven track record of productionising data and ML systems at scale.
Responsibilities
- Lead hire and mentor a highperforming team; set priorities delivery cadence and engineering standards to scale the capability.
- Own the roadmap and delivery of enterprise data platforms and analytics products including data lakes/warehouses ETL/ELT streaming and data APIs.
- Provide handson technical leadership: design and review architecture implement or optimise key components and resolve production incidents as required.
- Drive adoption of Databricks best practice (Spark optimisation Delta Lake Unity Catalog) and core cloud platform patterns.
- Productionise AI/ML: guide feature engineering model deployment monitoring versioning explainability and MLOps workflows.
- Establish and enforce CI/CD automated testing observability and incident response for data pipelines models and analytics services.
- Define and enforce data governance security privacy and compliance standards in partnership with legal and security teams.
- Partner with business leaders product managers and consulting teams to translate business problems into scalable data and AI solutions; manage stakeholder expectations and prioritisation.
- Manage vendor relationships and platform budgets; evaluate and procure thirdparty 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 largescale data systems; 5 years managing engineering teams and senior technical leaders.
- Proven experience delivering endtoend cloud data platform transformations at enterprise scale.
- Handson 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).
- Solid understanding of AI/ML production patterns including model development deployment monitoring drift detection and MLOps.
- 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 clientfacing delivery experience.
- Experience with streaming platforms (Kafka Pub/Sub Kinesis) and realtime architectures.
- Familiarity with generative AI LLMs and conversational AI production patterns.
- Exposure to other cloud providers (Azure/GCP) or hybrid cloud architectures.
Marsh McLennan (NYSE: MMC) is a global leader in risk strategy and people advising clients in 130 countries across four businesses: Marsh Guy Carpenter Mercer and Oliver Wyman. With annual revenue of $24 billion and more than 90000 colleagues Marsh McLennan helps build the confidence to thrive through the power of perspective. For more information visit or follow on LinkedIn and X.
Marsh McLennan 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 McLennan 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 McLennan 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.
Required Experience:
Director
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