Data Analytics Engineer II at Mercury Insurance Services, LLC

, , United States -

Full Time

Start Date

Immediate

Expiry Date

28 Jun, 26

Salary

179048.0

Posted On

30 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Modeling, dbt, Semantic Layer, Prompt Engineering, Root Cause Analysis, Metric Store, SQL, Python, Jinja Macros, YAML, Data Quality, Data Governance, API Layer, Columnar Warehouse, Pandas, Scripting

Industry

Insurance

Description

Overview Position Summary: 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. Geo-Salary Information An in-person interview may be required during the hiring process State specific pay scales for this role are as follows: $94,458 to $179,048 (CA, NJ, NY, WA, HI, AK, MD, CT, RI, MA) $X85,871 to $162,771 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME) $77,283 to $146,494 (UT, ID, MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV) The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location. Responsibilities Essential Job Functions: 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 toolsSupport 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 Qualifications Education: 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 haveP&C insurance domain experienceExperience with:Cohort analysisFunnel metricsPerformance analysisFamiliarity with MetricFlow specifically and the dbt Semantic LayerExposure to Retool or similar low-code tools for operational write-back workflowsFastAPI or similar Python API frameworks (Flask, Django REST) for serving data products as services About the Company Why choose a career at Mercury? 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. Learn more about us here: https://www.mercuryinsurance.com/about/careers Perks and Benefits We offer many great benefits, including: 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 Mercury Insurance is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by federal, state, or local law. Pay Range USD $94,458.00 - USD $179,048.00 /Yr.

Responsibilities

The role involves building and scaling the enterprise metrics store, developing and maintaining dbt models, and contributing to semantic layer definitions to ensure consistency of key business metrics. Responsibilities also include implementing analytical logic for root cause analysis and translating business questions into scalable analytical patterns for consumption across various tools.