Data Engineering Manager

Datasite - Minneapolis, MN

Full-time - Mid Level

Hybrid - Minneapolis, MN

Professional, Scientific, and Technical Services

About the position

The Data Engineering Manager at Datasite is responsible for architecting complex data solutions, developing data pipelines, and ensuring the availability of high-quality data at scale. This role involves leading a team to enhance the organization's data architecture and processes, translating business requirements into technical solutions, and driving initiatives to improve data quality and observability. The position is hybrid, requiring office visits in Minneapolis, MN.

Responsibilities

• Work with senior leadership and product teams to determine the roadmap for new data initiatives and organization-wide architecture standards.
• Translate business requirements from senior leadership into actionable technical requirements.
• Drive initiatives to enhance data architecture and standards for improved data quality and validity.
• Discover new data sources and integrate them into the platform.
• Suggest solutions to improve data observability across the organization.
• Write documentation for non-technical stakeholders.
• Explore and recommend new tools and technologies to optimize the data platform.
• Fully own the team's technical roadmap and manage direct reports.

Requirements

• Bachelor's Degree or equivalent industry experience.
• 6+ years of experience in Data Engineering or Data Architecture or a related role.
• Expert proficiency with data warehouses, including architecture and lifecycle.
• Prior experience as a team lead or manager.
• Familiarity with column-based storage (Snowflake).
• Exposure to non-relational data, data structures, and noSQL databases such as MongoDB.
• Experience leading major technical initiatives related to data or data tools.
• Proficiency with several ELT/ETL tools.
• Experience with Python.
• Some prior experience as a data modeler or in data governance is a plus.
• Exposure to a BI tool is beneficial.
• Experience predicting scaling requirements and compute needs.
• Comfortable with the structure of a data lake and flows between data stored in a lake, warehouse, and AI/ML processes.

Nice-to-haves

• Experience as a data modeler or in data governance.
• Exposure to a BI tool.