Senior Manager, Data & Analytics Engineering

Build and lead a team of Analytics Engineers and Data Engineers, setting technical standards across both disciplines

Drive rapid team scaling to expand the org to 6-8 engineers within a year, establishing clear career ladders as scope expands

Cultivate a culture of engineering rigor, code review, automated testing, and continuous delivery

Own the architecture and strategy for data ingestion, warehouse modeling, and the analytics engineering layer (dbt, semantic layer, marts)

Design and enforce data governance and security frameworks: role-based access control (RBAC), PII/PHI handling, masking, and tokenization in a regulated environment

Stay technically close enough to conduct code/architecture reviews, unblock technical bottlenecks, and step in personally on critical platform issues

Ensure every internal stakeholder group gets the right tooling and access model for their specific needs, from operational reps and executive leadership to technical power users in Finance, Credit Risk, and Practice Risk

Serve as the de facto product manager for data-related production systems and core infrastructure, translating requirements from internal partners into a unified, actionable roadmap

Define frameworks governing how decentralized internal applications and rapidly prototyped tools are built on Cherry's data surfaces, keeping self-service scalable and reliable

7+ years in data engineering and/or analytics engineering, including direct people management experience

Demonstrated ability to attract, hire, and scale engineering talent quickly while maintaining a high technical bar

Proven track record owning end-to-end data platform architecture from ingestion through to consumption layers

Experience operating as a product owner/liaison for technical platform tools, balancing business feature requests with technical debt and reliability

Background in a regulated industry (e.g., Fintech, Healthcare, Insurtech) navigating strict compliance and governance requirements is strongly preferred

Modern Stack: Deep expertise in cloud data warehouses (e.g., Snowflake), orchestration tools (e.g., Airflow), and ingestion tooling (e.g., Fivetran)

Analytics Engineering: Mastery of dbt and core analytics engineering practices — data modeling, semantic layer design, testing, and documentation best practices

Governance & Security: Practical experience designing role-based access control (RBAC) frameworks, PII/PHI tokenization, masking, and automated access control systems

Experience coaching across distinct technical role types, and building out senior/lead layers as a team scales

Able to represent technical tradeoffs and governance requirements clearly to both engineering peers and non-technical executive stakeholders

Strong prioritization instincts when governance work, platform reliability, and business-facing feature requests compete for the same roadmap slot