[Hiring] Data Analytics Engineer II @Mercury Insurance Services

Role Description

We’re looking for an Analytics Engineer II to build our next-generation enterprise metrics store and enable insights across underwriting, sales, product, claim and experience. This role blends analytics engineering and prompt engineering, supporting our journey toward a fully governed, AI-ready data ecosystem.

As an Analytics Engineer, you will sit at the intersection of:

• data modeling (dbt, semantic layer)
• business metrics (insurance domain: quotes, binds, premium, agency performance)
• analytics engineering (root cause analysis, metric relationships, metric store)

This is a hands-on role where you will design, build, and scale core metrics and analytical workflows, working closely with product, business, and engineering stakeholders. The team is evolving toward exposing metric infrastructure as internal services, so you’ll have the opportunity to shape that API layer as it matures.

Qualifications

• 3–5 years of analytics engineering or similar analytical role experience with dbt or similar transformation frameworks proficiency: models, tests, incremental materialization, Jinja macros
• Advanced SQL on a columnar warehouse (Redshift, Snowflake, or BigQuery)
• Python for data transformation and analysis (pandas, basic scripting)
• Comfort working with YAML-based configuration and version-controlled analytics workflows
• Clear written and verbal communication—able to explain metric definitions and data lineage to non-technical stakeholders

Nice to have:

• P&C insurance domain experience
• Experience with:
  • Cohort analysis
  • Funnel metrics
  • Performance analysis
• Familiarity with MetricFlow specifically and the dbt Semantic Layer
• Exposure to Retool or similar low-code tools for operational write-back workflows
• FastAPI or similar Python API frameworks (Flask, Django REST) for serving data products as services

Requirements

• Build and scale the metric layer
• Develop and maintain dbt models
• Contribute to semantic layer definitions (metrics, dimensions, relationships)
• Ensure consistency and correctness of key business metrics and metric hierarchies (metric pyramid)
• Implement analytical logic (root cause analysis & metric insights)
• Build root cause analysis workflows:
• Implement baseline comparisons
• Companion metric analysis
• Translate business questions into scalable analytical patterns
• Enable metric consumption across tools
• Support metric usage in different BI or analytical tools
• Build reusable logic that avoids duplication across tools
• Prepare for future API-based metric serving layer
• Partner with business and product stakeholders
• Work closely with sales, product, underwriting, claims, experience and other business teams
• Translate ambiguous questions into structured metrics and actionable insights
• Improve data quality and governance
• Define and enforce metric definitions, dimension standards, and data contracts
• Debug issues across upstream pipelines, semantic layer, and analytical outputs

Benefits

• Competitive compensation
• Flexibility to work from anywhere in the United States for most positions
• Paid time off (vacation time, sick time, 9 paid Company holidays, volunteer hours)
• Incentive bonus programs (potential for holiday bonus, referral bonus, and performance-based bonus)
• Medical, dental, vision, life, and pet insurance
• 401 (k) retirement savings plan with company match
• Engaging work environment
• Promotional opportunities
• Education assistance
• Professional and personal development opportunities
• Company recognition program
• Health and wellbeing resources, including free mental wellbeing therapy/coaching sessions, child and eldercare resources, and more