Manager, Data Engineering & Analytics

As the Manager of Data Engineering & Analytics , you will report to the Vice President of Business Intelligence and lead The Helper Bees' Data Engineering and Analytics team, responsible for both the technical execution and strategic direction of data across the company. Your leadership will be key in ensuring the team produces accurate, governed, and on-time data reports, invoices, and analyses that our internal leaders and external partners depend on. In this role, you will manage and develop a high-performing team of Data Engineers and Data Analysts, guiding their career growth, performance, and skill development. You will own how the team takes in, prioritizes, and delivers work, and you will set clear, achievable goals that ladder to the company's data objectives. A central part of this role is helping the team move up the analytics maturity curve — from descriptive reporting toward **predictive and prescriptive analytics**. You will guide the team as we build machine-learning models to strengthen forecasting and develop algorithms such as recommendation engines and clustering/segmentation to understand members with similar diagnoses and needs profiles. You don't need to be a research scientist, but you must be technically literate enough in modern ML and data science to set direction, review approaches, partner with our data scientists, and grow analysts into this work. While setting the strategic vision for the data team, you will actively participate in executing the data strategy, rolling up your sleeves when needed - reviewing designs, unblocking pipelines, and making the hard tradeoff calls. As a people manager, you will foster an environment of mentorship, collaboration, and continuous learning, ensuring your team is empowered to fulfill the broader company vision, including our push to make AI a genuine multiplier for how the team works. You will work closely with leaders from Product, Accounts, Operations, and the C-suite - as well as external clients and partners - to ensure alignment and successful delivery of the data strategy, acting as both a leader and an advocate for the data function across the organization. Because The Helper Bees operates in healthcare, you will do all of this in an environment that requires careful handling of protected health information (PHI). Requirements Supervisory Responsibilities: Data Analysts/Business Analysts Duties/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 trade-offs, 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. Performance Metrics: Data Quality: Ensure high standards of data accuracy, completeness, and consistency by setting targets for improving data quality and enforcing data governance standards. Timeliness of Reporting: Lead the team in reducing reporting turnaround times to support timely decision-making and operational efficiency. Technical Leadership: Set clear technical directions and goals for the team. Evaluate your own and your team’s effectiveness in influencing stakeholders, mentoring team members, and adopting strategic initiatives. Code Quality and Maintainability: Monitor and assess the quality of code produced by the team, aiming for high standards of readability, scalability, and maintainability. Problem Solving and Innovation: Track the team’s ability to debug complex issues and foster a culture of innovation by recognizing creative and efficient technical solutions. Process Improvement: Measure the impact of efforts to refine and improve development processes, with a focus on enhancing the team’s productivity and efficiency. Stakeholder Satisfaction: Gather and evaluate feedback from stakeholders to assess satisfaction with data products and services, identifying areas for improvement and driving enhancements to stakeholder experiences. Scalability and Optimization: Oversee the scalability and optimization of data systems, setting goals to accommodate growing data volumes and user demands. Required Skills/Abilities: 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 with Tableau, Salesforce data, and Azure/Synapse a plus (PostgreSQL, PowerBI nice-to-have). 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). Hands-on ML experience and exposure to deploying/monitoring models in production strongly preferred. Familiarity with, or willingness to drive, AI-assisted development and analytics tooling. 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. 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. Education and Experience: 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) preferred. #J-18808-Ljbffr The Helper Bees