Note: The job is a remote job and is open to candidates in USA. The Helper Bees is a healthcare-focused company that helps older adults find and access quality, affordable in-home care providers. The Manager of Data Engineering & Analytics will lead the Data Engineering and Analytics team, oversee data strategy and delivery, and advance governed reporting, predictive analytics, machine learning, and AI initiatives. The role also manages team development, stakeholder collaboration, data quality, and the reliable delivery of reports, invoices, and analyses involving sensitive health information.
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
- Other duties as assigned or necessary to support team and company success
Skills
- 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)
- Familiarity with, or willingness to drive, AI-assisted development and analytics tooling
- 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
- Ability to remain at your designated workstation for the duration of the workday
- Constantly operates a computer and other office productivity machinery, such as a phone and Voice over Internet Protocol (VoIP)
- The ability to communicate information and ideas so others will understand. Must be able to exchange accurate information in these situations
- The ability to observe details at close range (typically on a computer screen)
- This position offers the flexibility of remote work at approved locations within the United States. Candidates must have a reliable internet connection and a designated work environment conducive to professional phone calls and sensitive data
- Experience with Tableau, Salesforce data, and Azure/Synapse a plus
- 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). Hands-on ML experience and exposure to deploying/monitoring models in production strongly preferred
- Experience managing or transitioning work from external consultants/vendors a plus
- Experience handling sensitive data responsibly; familiarity with HIPAA / PHI handling is strongly preferred given our healthcare context
- Experience delivering — or leading a team to deliver — predictive/prescriptive analytics or machine-learning models (e.g., forecasting, recommendation, or clustering/segmentation) preferred
Benefits
- Flexible remote work at approved locations within the United States
Company Overview