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Data Analytics Engineer II

Mercury Insurance LogoMercury Insurance
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Salary

$94,458 - $179,048

Location

Remote

Posted

Yesterday

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.

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 and metric hierarchies (metric pyramid).
  • Implement analytical logic for root cause analysis and metric insights.
  • Build root cause analysis workflows, including baseline comparisons and companion metric analysis.
  • Translate business questions into scalable analytical patterns.
  • Enable metric consumption across tools and build reusable logic that avoids duplication across tools.
  • Prepare for a future API-based metric serving layer.
  • Partner with business and product stakeholders across sales, product, underwriting, claims, and experience.
  • Translate ambiguous questions into structured metrics and actionable insights.
  • Improve data quality and governance by defining and enforcing metric definitions, dimension standards, and data contracts.
  • Debug issues across upstream pipelines, the semantic layer, and analytical outputs.

Qualifications

Education:

  • 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 with models, tests, incremental materialization, and 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, and performance analysis.
  • Familiarity with MetricFlow 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.

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