Data Engineering Manager

Data Engineering Manager

Leadership & People Management

• Lead, mentor, and develop a team of Data Engineers and technical professionals.
• Foster a collaborative, accountable, and high-performing engineering culture.
• Participate in hiring, onboarding, coaching, performance management, and career development initiatives.
• Partner with technology and business leaders to define and execute data engineering priorities.
• Help mature and scale the Data Engineering function as the organization continues to grow.

Data Engineering & Architecture

• Design, build, and optimize scalable end-to-end data pipelines.
• Lead efforts around enterprise data warehousing and data architecture best practices.
• Implement and maintain modern ELT/ETL frameworks and processes.
• Develop and support dimensional models, business data models, and analytics-ready datasets.
• Establish standards for data quality, governance, lineage, and modeling.
• Create source-to-target mappings and oversee data integration initiatives.
• Drive best practices around Data Vault 2.0, Kimball methodology, and modern lakehouse/data warehouse architectures.

Strategy & Cross-Functional Partnership

• Collaborate with senior leaders and department stakeholders to understand business needs and data challenges.
• Translate business requirements into scalable technical solutions.
• Support the evolution of the data platform and cloud modernization initiatives.
• Help define long-term data management, governance, and analytics strategies.
• Partner with cross-functional teams to prioritize data engineering initiatives and deliver business value.

Hands-On Technical Contribution

• Remain actively involved in technical design, development, troubleshooting, and delivery.
• Contribute code, review solutions, and support engineering efforts when needed.
• Act as a technical leader and subject matter expert for the team.
• Provide technical guidance and mentorship to Data Engineers.
• Help establish engineering standards, best practices, and development processes.

Required Qualifications

• 7+ years of experience in Data Engineering, Data Warehousing, or related disciplines.
• 3+ years of people leadership or management experience.
• Proven experience building, mentoring, and developing engineering teams.
• Strong expertise designing and supporting enterprise data warehouse solutions.
• Experience with modern orchestration and transformation tools, including:
• Apache Airflow
• dbt and/or Coalesce
• Deep understanding of:
• Data Vault 2.0
• Kimball Methodology
• Dimensional Modeling
• Data Lake architectures
• Data Governance frameworks
• Experience designing and supporting end-to-end data pipelines.
• Experience creating source-to-target mappings and enterprise data models.
• Strong understanding of data architecture principles and best practices.
• Excellent communication skills with the ability to engage both technical and business stakeholders.
• Demonstrated ability to operate effectively as both a people manager and hands-on technical contributor.

Preferred Qualifications

• Experience working within financial services, banking, or highly regulated industries.
• Experience with Snowflake.
• Experience supporting enterprise analytics platforms and reporting environments.
• Experience with Sigma, Cognos, SQL Server, or similar technologies.
• Exposure to Alteryx.
• Experience leveraging AI-assisted development tools such as Claude Code, Cortex, Copilot, or similar platforms.

Technology Environment

Current and evolving technologies include:

• Snowflake
• Apache Airflow
• dbt
• Coalesce
• SQL Server
• Cognos
• Sigma
• Enterprise Data Warehousing Technologies