Data Analytics Engineer II

Mercury Insurance is a company dedicated to helping people manage risk and overcome unexpected events. They are seeking an Analytics Engineer II to build and scale the enterprise metrics store, enabling insights across various business functions such as underwriting and sales. This role involves data modeling, analytics engineering, and collaboration with product and business stakeholders to ensure high-quality data governance and metric definitions.

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 o 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:

• Bachelor's Degree in Computer science, Statistics or similar
• 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
• 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