Data Analytics Engineer II at Mercury Insurance

About the role

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.

About the Role

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.

Key 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
Implement analytical logic (root cause analysis & metric insights)
Build root cause analysis workflows: Implement baseline comparisons and 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

Qualifications

3–5 years of analytics engineering or similar analytical role experience
Proficiency with dbt or similar transformation frameworks (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
Exposure to FastAPI or similar Python API frameworks

About Mercury Insurance

At Mercury, we have been guided by our purpose to help people reduce risk and overcome unexpected events for more than 60 years. We are one team with a common goal to help others. Everyone needs insurance and we can’t imagine a world without it.

Our team will encourage you to grow, make time to have fun, and work together to make great things happen. We embrace the strengths and values of each team member. We believe in having diverse perspectives where everyone is included, to serve customers from all walks of life.

We care about our people, and we mean it. We reward our talented professionals with a competitive salary, bonus potential, and a variety of benefits to help our team members reach their health, retirement, and professional goals.

Perks And 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
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

Key skills/competency

Data Analytics Engineer
dbt
SQL
Python
Data Modeling
Semantic Layer
Metric Store
Root Cause Analysis
Insurance Domain
API Development

How to get hired

Tailor your resume: Highlight dbt, SQL, Python, and data modeling skills. Emphasize insurance domain experience if applicable.
Showcase analytics engineering: Detail experience with metric layers, root cause analysis, and semantic layers in your application.
Prepare for technical interviews: Brush up on advanced SQL, dbt concepts, and Python for data transformation.
Research Mercury's culture: Understand their 60-year mission and focus on teamwork and diversity.
Craft a strong cover letter: Express your interest in building an AI-ready data ecosystem and enabling cross-functional insights.

Frequently asked questions

What is the salary range for a Data Analytics Engineer II at Mercury Insurance?

The salary range for a Data Analytics Engineer II at Mercury Insurance varies by location. In states like CA, NJ, NY, WA, HI, AK, MD, CT, RI, and MA, the range is $94,458 to $179,048. For states like NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, and ME, it's $X85,871 to $162,771. For other states, the range is $77,283 to $146,494. The final salary will depend on experience, skills, and specific location.

Is this a remote or on-site Data Analytics Engineer II position at Mercury Insurance?

While many positions at Mercury Insurance offer flexibility to work from anywhere in the United States, an in-person interview may be required during the hiring process. The role itself appears to offer remote work flexibility for most positions.

What specific technical skills are most important for the Data Analytics Engineer II role at Mercury Insurance?

The most critical technical skills for this Data Analytics Engineer II role include proficiency in dbt or similar transformation frameworks, advanced SQL on columnar warehouses (Redshift, Snowflake, BigQuery), and Python for data transformation and analysis (pandas, scripting). Experience with YAML-based configuration and version control is also important.

Does Mercury Insurance offer benefits for a Data Analytics Engineer II?

Yes, Mercury Insurance offers a comprehensive benefits package for its employees, including competitive compensation, bonus potential, medical, dental, vision, life, and pet insurance, a 401(k) retirement savings plan with company match, paid time off, and opportunities for professional development and education assistance.

What does 'AI-ready data ecosystem' mean in the context of the Data Analytics Engineer II job description at Mercury Insurance?

An 'AI-ready data ecosystem' means that the data infrastructure and processes are designed to support and facilitate the use of Artificial Intelligence and Machine Learning. For this role, it involves building a well-governed metrics store and ensuring data quality and consistency, which are foundational for reliable AI model training and deployment.

What kind of experience is preferred for the Data Analytics Engineer II role at Mercury Insurance?

While not strictly required, experience in the P&C (Property and Casualty) insurance domain is preferred. Additionally, familiarity with specific tools like MetricFlow and the dbt Semantic Layer, as well as experience with low-code tools like Retool and API frameworks like FastAPI, would be beneficial.

How does Mercury Insurance support career growth for a Data Analytics Engineer II?

Mercury Insurance supports career growth through promotional opportunities, professional and personal development programs, and education assistance. They foster an engaging work environment that encourages growth and collaboration.

Data Analytics Engineer II

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