Senior Analytics Engineer

Join our winning team, recently honored as on Forbes’ list of America’s Best Startup Employers for 2025!

The Helper Bees (THB) was created to fill an obvious need in an underserved community. Inspired by love and brought to reality through passion and determination, The Helper Bees was founded to empower older adult citizens and their families in their search for quality, affordable in-home care providers. We do this by providing older adults the ability to easily review, choose, and access affordable quality in-home helpers.

The Helper Bees mission is to be the best in the world at finding & fulfilling the needs of older adults.

At THB, we define our company culture through our Core Values:

• Quickly iterate through solutions - We move at a fast pace which requires quick iterations to find a path to a repeatable solution
• Seek ways to create immediate impact - Be thoughtful and proactive in how you make an impact on your team. Actively look for ways to make a fast, positive impact.
• Bee the teammate you want to work with - We work as a team, help each other and encourage each other
• Ask questions, answer questions - You can't iterate through solutions if you don't ask the right questions which is why there is an expectation that questions should be asked. When you know the answer, being a good teammate means chiming in to get others up to speed.
• Take the time to celebrate wins - It's so easy for a team that is heads down to forget about all the great things they've accomplished. That's why we make it a priority to remind ourselves to create space to celebrate wins, big or small.

Job Summary

The Senior Analytics Engineer owns problems end-to-end — from ambiguous business questions to stable, scalable technical solutions.This role sits at the intersection of Product, Operations, and Business Strategy. You will partner with senior stakeholders to define success metrics, design the right technical approach, and build production-ready data systems that drive measurable outcomes.

This is not a dashboard-only role, and it’s not purely academic modeling. It’s a systems-building role.

You will translate business needs into well-modeled data foundations, experimentation frameworks, automation pipelines, and analytical products that improve funnel performance and operational efficiency. Solutions should be:

• Business-aligned
• Right-sized (not over-engineered)
• Scalable and generalizable
• Trusted, documented, and reproducible

You will act as both technical architect and strategic thought partner — independently identifying high-impact opportunities and delivering durable data systems that the organization can rely on and extend over time.

Supervisory Responsibilities

• No direct reports
• Mentor and guide junior data analysts and business analysts
• Establish modeling, documentation, and data quality standards
• Provide technical review and feedback across analytics initiatives

Key Responsibilities

• Partner directly with Product, Engineering, Operations, and senior leadership to translate functional requirements into structured technical plans and scalable data solutions
• Architect and maintain clean, well-documented data models using dbt or similar transformation frameworks
• Design advanced SQL transformations and optimized queries within modern cloud data warehouses (Azure, Google Cloud stack experience a plus)
• Build and productionize Python-based analytical workflows including:
  
  • Funnel analysis and conversion optimization
  • Lifecycle and churn modeling
  • Segmentation
  • Predictive modeling
  • Experimentation frameworks
    
• Lead A/B test design, statistical evaluation, and causal analysis to measure product and operational impact
• Design and automate durable data marts and core datasets that serve as single sources of truth
• Improve product funnel flows and identify actionable opportunities to increase conversion
• Develop automations that reduce manual reporting and increase leverage across teams
• Translate complex analytical findings into clear, actionable recommendations for non-technical stakeholders and executive leadership
• Shape KPI definitions, experimentation standards, and metric governance
• Drive adoption of analytics engineering best practices across teams
• Identify and independently execute high-impact analytical initiatives

Performance Metrics

• Strategic Impact: Percentage of initiatives supported by analytics that result in measurable improvements to product, operational efficiency, or revenue
• Conversion & Funnel Impact: Measurable improvements in user progression through key product flows
• Data Trust & Adoption: Stakeholder reliance on core datasets and experimentation frameworks
• Execution Quality: Projects delivered on time, clearly documented, with minimal rework

Requirements Accuracy: =95% of projects completed without significant requirement gaps post-approval

Required Skills & Experience

• 5+ years in analytics engineering, product analytics, or data science roles
• Demonstrated ability to translate ambiguous business problems into structured, scalable technical solutions
• Advanced SQL proficiency (complex transformations, performance optimization, modeling)
• Strong Python skills (pandas, statistical modeling, experimentation workflows, ML libraries)
• Experience building production-ready data models using dbt or similar tools
• Experience working with modern cloud data stacks (Azure or Google Cloud preferred)
• Deep understanding of experimentation design, causal inference, churn, and conversion analysis
• Experience working cross-functionally with Product, Engineering, and Operations teams
• Strong documentation practices and commitment to data quality
• Exceptional communication skills; able to clearly explain technical findings to C-suite and senior director–level stakeholders
• Ability to operate independently and drive initiatives without heavy oversight

Preferred Experience

• Experience in SaaS, marketplace, or tech-enabled services environments
• Experience improving product funnels, user experience, and conversion flows through data
• Experience working with contact center data or operational teams
• Experience with Salesforce data
• Experience with healthcare data, senior care, insurance, or Medicare Advantage
• Background in behavioral science, economics, applied statistics, psychology, or quantitative research
• Experience using AI tools (e.g., Claude Code or similar) to accelerate analytics workflows and experimentation

Education

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Psychology, Economics, or related quantitative field
• Master’s or PhD preferred, though strong practitioners with demonstrated impact are strongly considered

The Helper Bees is committed to building a workplace where diversity, equity, and inclusion are valued and prioritized. We are an equal opportunity employer that welcomes all qualified applicants without discrimination based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any non-merit based or legally protected grounds.

The Helper Bees provides reasonable accommodations to qualified individuals with disabilities during the job application and interview process. To request accommodation, please let your recruiter know.

As part of our standard hiring process, selected candidates may be required to undergo a background check and/or drug screen.  The Helper Bees adheres to applicable federal, state, and local laws regarding these screenings, and the results will be considered in accordance with applicable regulations.    

The Helper Bees was recently made aware of a fraudulent entity posing as our organization and requesting personal information. Please be aware of and protect yourself from scams. Visit the careers page of our website to view all current job openings.