Role: Data warehouse Manager
Location: Onsite at Columbus ,Ohio and no remote.
Type: Contract-to-hire
Openings: 1
Dates: Nov 23, 2026 – Mar 2, 2027 (goal is to convert after this contract)
Sponsorship: Not available
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What we're looking for:
A hands-on data warehouse leader who will manage a team of 12–15 (employees and contractors) within Enterprise Data Warehouse.
10+ years of hands-on engineering on multi-terabyte data warehouse projects and 7+ years managing data warehouse technology teams. Both are required.
Strong in traditional EDW architecture: ODS, sourcing/staging, data integration, MDM, dimensional/snowflake modeling, datamarts.
Cloud experience with AWS and Snowflake; DataLake, Python/PySpark and Erwin are pluses.
Data governance and data quality experience, plus the communication skills to work with the Chief Data & Analytics Office and business leaders.
Bachelor's degree required.
Job Description:
We are looking for a data warehouse leader in our Data Technology organization. In this role you will lead a team of 12-15 dedicated to pushing the limits of continuous improvement and innovation using data warehousing as a lever. As a Sr. Manager of the Data Warehouse, you will report to the Data Warehouse Director and work closely with the Chief Data & Analytics Office to influence the business and technology strategies for the company.
If you consider data as a strategic asset, evangelize the value of good data and insights, have deep understanding of data governance, are an experienced thought leader in data warehousing and love building teams and mentoring talent, this role is for you.
You will be responsible for leading within the Enterprise Data Warehouse for the bank, including innovating on the platform to enable next-generation capabilities. You will participate in the definition of the strategic roadmap for data technology and be responsible for executing data warehousing initiatives. You will partner with other Data Technology organizations and CDAO leads to architect and enable an effective data ecosystem, as well as key leaders in business and technology to understand and support their needs.
Duties and Responsibilities:
Manage a team of colleagues and contractors, providing resource allocation, coaching and development.
Lead a cross functional agile team to deliver on projects, providing thought leadership and technology oversight.
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 the 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.
Basic Qualifications:
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 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.
Proven ability to take requirements and logical designs and translate them into physical implementation, 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.
