Data Analytics Engineer II

Mercury Insurance is dedicated to helping people reduce risk and overcome unexpected events. They are seeking an Analytics Engineer II to build a next-generation enterprise metrics store and enable insights across various business domains. This hands-on role involves designing, building, and scaling core metrics and analytical workflows while collaborating with product, business, and engineering stakeholders.

Responsibilities:

• 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
• 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
• Actionable insights
• Improve data quality and governance
• Define and enforce:
• Metric definitions
• Dimension standards
• Data contracts
• Debug issues across:
• Upstream pipelines
• Semantic layer
• Analytical outputs

Requirements:

• 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
• P&C insurance domain experience
• 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