Data Warehouse Sr. Manager

Summary

Huntington Bank is seeking a Data Warehouse Sr. Manager in its Data Technology organization. This role involves leading a team of 12-15 professionals focused on continuous improvement and innovation through data warehousing. The Sr. Manager will report to the Data Warehouse Director and collaborate with the Chief Data & Analytics Office to shape the company’s data technology strategies.

Duties and Responsibilities

• Manage a team of colleagues and contractors, allocating resources, providing coaching, and developing talent.
• Lead a cross-functional agile team to deliver projects, offering thought leadership and technology oversight.
• Participate in demand management and planning, assessing impact, feasibility, and estimating work effort.
• Translate technical designs from the Data Architect team into implemented physical data models that align with data governance, enterprise architecture, and business requirements for data warehousing.
• Collaborate with operational data and data acquisition teams to manage incoming sources and downstream systems, supporting reporting and analytics needs.
• Support incident resolution and continuous improvement efforts to enhance operational run metrics.

Basic Qualifications

• Bachelor's Degree
• 10+ years of hands-on experience in multi-terabyte data warehousing engineering projects.
• 7+ years of experience as a technology manager for data warehouse teams.

Preferred Qualifications

• 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 the ability to build and manage a high-performing team.
• Strong communication and interpersonal skills for interacting and collaborating with developers, analysts, and business colleagues.
• Proven ability to translate requirements and logical designs into physical implementations, informed by business and enterprise requirements.
• Experience with traditional data architecture: operational data stores, sourcing and staging data, data integration, master data management, dimensional/snowflake and de-normalized design, managed views, datamarts, and analytical sandboxes.
• Experience with cloud technologies including AWS and Snowflake.
• Experience with DataLake, Python/PySpark, Erwin Enterprise Data Modeler.
• Experience with data governance and data management approaches, including data quality.
• Experience with business intelligence and advanced analytics.