Manager, Data Engineering & Analytics

The Helper Bees is a company dedicated to empowering older adult citizens and their families in finding quality, affordable in-home care providers. As the Manager of Data Engineering & Analytics, you will lead the Data Engineering and Analytics team, ensuring the production of accurate and timely data reports while guiding the team's technical and strategic direction.

Responsibilities:

• Lead and manage the Data Engineering & Analytics team, providing mentorship, guidance, and support to ensure technical and professional development across a team of mixed tenure, including members still onboarding
• Design, implement, and maintain The Helper Bees' data products using Python, SQL, dbt, Airflow, BigQuery, and other modern technologies
• Own the team's intake and prioritization — how requests come in, get triaged, and get scheduled (managed in Jira) — and make that process legible to stakeholders so they can see where their requests stand and why
• Champion a dbt-first, governed approach: move reporting off manual and bespoke SQL into tested, traceable, automated models, and hold the team to a high reliability bar (on-time delivery and zero manual edits to production data)
• Lead the team's expansion into predictive and prescriptive analytics — guiding the development of ML models for forecasting, recommendation engines, and clustering/segmentation to profile members by diagnosis and need — and put the practices in place (validation, monitoring, governance) to deploy and maintain these models responsibly in a healthcare context
• Set strategic goals for the team, ensuring alignment with the company's data objectives and promoting continuous improvement
• Collaborate with department teams to design, build, test, and implement new features and enhancements that meet internal and external stakeholder needs
• Monitor team performance and development, fostering an environment of growth through regular feedback, code reviews, and training opportunities
• Produce quality, testable, and maintainable code, while setting high coding standards for the team
• Assist in refining and improving development processes such as deployment, sprint planning, monitoring, incident response, escalation, and team workflows
• Drive adoption of AI tooling (e.g., Cursor, Claude Code) as a multiplier for the team's engineering and analytics work — modeling personal use, and making AI experimentation and sharing a standing team practice
• Facilitate problem-solving and innovation within the team, encouraging creative solutions to complex technical challenges
• Act as a technical leader for the team, growing other engineers and analysts through mentorship and example, while ensuring high-quality, scalable, and optimized data systems
• Quickly isolate and debug complex issues across data pipelines and models, helping the team troubleshoot problems effectively
• Collaborate with stakeholders, product teams, operations, external clients, and partners to understand requirements, discuss tradeoffs, and deliver viable solutions
• Own the data team's contribution to the monthly close and scorecard — client and partner reports and invoices delivered accurately and on time — and surface exceptions proactively
• Drive process improvements within the team, measuring the impact on delivery reliability, efficiency, and overall productivity

Requirements:

• Proven experience in crafting, communicating, and promoting strategic direction for a data team
• Strong leadership and team management skills, with experience in mentoring and developing a high-performing technical team, including people still early in their careers
• Ability to lead change management initiatives and influence key stakeholders across functions
• Strong data analysis and presentation skills, with experience in building and improving data warehouses and modern data pipelines
• Extensive experience with SQL and data engineering, including ETL/ELT solutions, and hands-on expertise with modern tooling — BigQuery, dbt, Airflow, and Python required
• Experience owning an intake and prioritization process (e.g., in Jira) and making a backlog transparent to stakeholders
• Working knowledge of machine learning and data science sufficient to set direction and review approaches — including forecasting/time-series methods, recommendation engines, and clustering/segmentation — and the Python ecosystem used to build them (e.g., pandas, scikit-learn)
• Excellent communication and interpersonal skills, with a passion for guiding others and working directly with non-technical and external stakeholders
• Must be 18 years of age or older
• Bachelor's degree in Computer Science, Engineering, Mathematics, Information Systems, or a related field. Relevant work experience may be considered in lieu of a degree
• 5+ years of experience in data analytics, data engineering, or a related data discipline, with increasing scope and responsibility
• 2+ years of hands-on experience as a Data Engineer or in a highly technical data role (SQL, dbt, and pipeline orchestration such as Airflow)
• 2+ years of direct people-leadership experience, including hiring, coaching, performance management, and driving team execution
• Demonstrated experience leading complex data initiatives from strategy through delivery, including migrations or platform transitions
• Experience delivering — or leading a team to deliver — predictive/prescriptive analytics or machine-learning models (e.g., forecasting, recommendation, or clustering/segmentation)
• Experience with Tableau, Salesforce data, and Azure/Synapse a plus (PostgreSQL, PowerBI nice-to-have)
• Experience managing or transitioning work from external consultants/vendors
• Familiarity with, or willingness to drive, AI-assisted development and analytics tooling
• Hands-on ML experience and exposure to deploying/monitoring models in production strongly preferred
• Experience handling sensitive data responsibly; familiarity with HIPAA / PHI handling is strongly preferred given our healthcare context