Manager Data Management

About the role

The Manager, Data Management will lead the design, delivery, and evolution of the enterprise data platform, with a strong focus on Master Data Management (MDM) as part of a modern data strategy. You will oversee Azure‑based big‑data solutions, guide both in‑house and outsourced teams, and ensure the reliability of all data applications, databases, and services.

Key responsibilities

• Lead architecture, implementation, support and continuous improvement of enterprise data platforms, including MDM solutions.
• Own technical direction for the MDM platform – data models, integration patterns, survivorship rules, matching, workflow, stewardship and data‑quality processes.
• Define long‑term architecture for modern big‑data platforms such as data lakes, lakehouses, delta lakes and cloud‑native processing frameworks.
• Provide leadership to data engineers and developers across design, development, deployment, operations and production support.
• Design and manage integrations between data platforms, operational systems and analytical tools using APIs, ETL/ELT pipelines, Azure Data Factory, Function Apps and Logic Apps.
• Establish architecture standards and best‑practice guidelines for ingestion, transformation, orchestration, storage, semantic modeling, observability and security.

Required profile

• BA/BS or master’s degree in Computer Science, Information Systems, Engineering or related field.
• 7+ years of progressive experience in data architecture, data engineering, big‑data platforms or MDM.
• 5+ years designing and delivering cloud‑based data solutions on Microsoft Azure or comparable platforms.
• Proven experience building modern enterprise data platforms (data lakes, lakehouses, delta lakes, scalable storage, distributed processing).
• Hands‑on experience with Azure Data Lake Storage, Azure Databricks and related cloud data technologies.

Required skills

• Azure Data Lake Storage
• Azure Databricks
• Azure Data Factory
• Azure Function Apps
• Azure Logic Apps
• Master Data Management (MDM)
• Data modeling and integration patterns
• ETL/ELT pipelines
• APIs and data‑quality processes
• Delta Lake, Data Lakehouse, cloud‑native processing frameworks