Lead Analytics Engineer

Responsibilities

• The Lead Analytics Engineer is responsible for establishing the Analytics Engineering discipline.
• This role defines and evolves the data modeling patterns, business logic frameworks, and implementation standards that guide the team's work.
• You remain hands-on in design and implementation, providing technical direction and serving as the authority on complex data decisions.
• Enterprise Data Products & Architecture: Lead the design and stewardship of enterprise data products, analytical data models, shared metrics, semantic definitions, and reference data.
• Define and evolve the Analytics Engineering architecture, data modeling patterns, and implementation practices the team follows.
• Guide technical design decisions and evaluate tradeoffs related to scalability, maintainability, quality, performance, and long-term support.
• Establish review practices for significant changes to core data assets, metrics, dimensions, and business rules.
• Standards, Governance & Quality: Establish and evolve standards for data modeling, transformations, testing, validation, documentation, naming conventions, and semantic definitions.
• Promote consistent use of standards and reusable assets through technical reviews, guidance, and mentorship.
• Establish quality control and change management practices that improve the reliability and maintainability of data assets.
• Lead complex troubleshooting and root cause analysis efforts.
• Analytics Engineering Delivery: Partner with Data Engineers, Analytics Engineers, and business stakeholders to deliver scalable and sustainable data solutions.
• Provide technical direction on solution design, implementation approaches, and technical prioritization.
• Identify opportunities to improve development processes, architecture, reuse, and long-term supportability.
• Ensure business logic, metrics, and semantic definitions are implemented consistently across data assets and analytical models.
• Leadership: Provide technical leadership, mentorship, and guidance to Analytics Engineers.
• Serve as the technical escalation point for complex Analytics Engineering challenges.
• Help establish a culture of technical discipline, thoughtful design, and continuous improvement.

Qualifications

• Experience working within insurance, brokerage, financial services, or other complex data-intensive industries.
• Advanced SQL and data modeling experience.
• Experience designing and maintaining enterprise-scale analytical data models.
• Experience implementing business logic, metrics, semantic definitions, and reusable data assets.
• Experience working with modern cloud-based analytics and data platforms, such as Databricks.
• Strong understanding of data quality, validation, testing, and governance practices.
• Experience mentoring technical team members and leading solution design efforts.
• Strong analytical and problem-solving skills.
• Ability to balance business needs, maintainability, scalability, and long-term support considerations.

Success Looks Like

• Shared business logic is reused rather than recreated.
• Standards and processes are consistently followed across the discipline.
• Data models, business rules, and semantic definitions remain maintainable and well documented.
• Analytics Engineers have clear technical direction and guidance.
• New solutions are built using common patterns rather than one-off approaches.
• Enterprise metrics, dimensions, and business rules remain consistent across data assets.
• Analytics Engineering practices continue to scale as adoption and demand grow.
• Data assets are easier to maintain, support, and evolve over time.