Enterprise Data Warehouse Manager

Key Responsibilities

• Lead a team of 8-12 resources, providing strategic direction, coaching, and technical oversight.
• Responsible for Project Delivery
• Drive agile execution, ensuring consistent velocity, transparency, and stakeholder engagement.
• Execute the EDW technology strategy, ensuring alignment with enterprise goals and segment priorities.
• Participate in demand management and planning, assessing impact, feasibility and estimating work effort.
• Understand and translate the technical design from the Data Architect team into implemented physical data models that meet data governance, enterprise architecture and business requirements for data warehousing. Manage data within the data warehouse to ensure efficiency of platform.
• Work with operational data and data acquisition teams to manage incoming sources and the down-stream systems to understand and support their needs for reporting and analytics.
• Support incident resolution and continuous improvement to drive efficiency across multiple operational run metrics.
• Perform other duties as assigned.

• 12+ years of hands-on experience in multi-terabyte data warehousing engineering projects.
• 7+ years of experience as a technology manager for data warehouse teams.
• Deep understanding of enterprise data warehousing best practices for technical implementation and providing business value. Thought leadership in next-evolution modern data architectures. Best practice EDW testing and validation approaches and implementation.
• Strong organizational skills with demonstrated ability to build and manage a high-performing team, work with partners to set and meet business service level agreement levels, plan and deliver work to expectations.
• Strong communication and interpersonal skills for interacting and collaborating with developers, analysts and business colleagues throughout the organization.
• Experience with cloud technologies including AWS and Snowflake.
• Experience with Databricks.
• Experience with data governance and data management approaches, including data quality.