Design, build and maintain scalable dbt models and data marts supporting product analytics use case across web, mobile and agent facing platforms.
Develop a unified data model integrating event data (Segment.io).
Implement clean metric logic (sessionization, funnels, conversions, retention) with clear definitions and metric lineage.
Build and automate ELT pipelines (using dbt) feeding the product analytics warehouse (Redshift).
Enforce data quality through schema tests, assertions and anomaly detection frameworks.
Maintain metadata and documentation to ensure transparency in the analytics layer.
Partner with engineering teams to define data contracts and ensure reliable event instrumentation across various products.
Work closely with analysts and product managers to translation business requirements into technical data models with reusable transformations.
Build and maintain dynamic sales funnel dashboards (using PowerBI), providing real-time insights into lead progression, conversion rates, and KPIs.
Take ownership of key analytics areas, including multi-raters, aggregators, and customer behavior in the insurance sales funnel.
Apply strong knowledge of SQL, Python, and data visualization to solve complex data problems.
Leverage understanding of the insurance industry to provide meaningful insights and guide product decisions, particularly in areas related to the insurance sales process.

Requirements

Bachelor’s degree in computer science, Mathematics, Statistics, Data Science, Business Analytics, or a related field.
5+ years of experience in data analytics, analytics engineering, data engineering.
4+ years of experience in product analytics, focusing on user behavior, engagement metrics, and product performance analysis.
Strong background in developing dashboards, reporting tools, building data models.
Proficiency in SQL and Python for data analysis and building data pipelines.
Strong experience with data visualization tools (PowerBI) to create and maintain comprehensive dashboards.
Expertise in relational and non-relational databases and data sources.
Deep understanding of ETL/ELT processes and tools (e.g., dbt).
Strong analytical and creative thinking, comfortable working in ambiguous situations.
Demonstrated expertise in data mining.
Excellent problem-solving skills and critical thinking abilities.
Strong written and verbal communication skills, capable of effectively conveying complex information.
Ability to interact and collaborate with senior management and cross-functional teams.
Passion for data-driven decision-making and continuous improvement in analytics practices.

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 (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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythondata visualizationdbtdata modelingELT pipelinesdata qualityschema testsanomaly detectiondata mining

Soft Skills

analytical thinkingcreative thinkingproblem-solvingcritical thinkingcommunicationcollaborationownershipadaptabilitytranslating business requirementspassion for data-driven decision-making

Certifications

Bachelor’s degree in computer scienceBachelor’s degree in MathematicsBachelor’s degree in StatisticsBachelor’s degree in Data ScienceBachelor’s degree in Business Analytics