Data Engineering and Analytics Manager

At the forefront of health tech innovation, CopilotIQ+Biofourmis is transforming in-home care with the industry's first AI-driven platform that supports individuals through every stage of their health journey-from pre-surgical optimization to acute, post-acute and chronic care. We are helping people live healthier, longer lives by bringing personalized, proactive care directly into their homes. With CopilotIQ's commitment to enhancing the lives of seniors with chronic conditions and Biofourmis' advanced data-driven insights and virtual care solutions, we're setting a new standard in accessible healthcare. If you're passionate about driving real change in healthcare, join the CopilotIQ+Biofourmis Team!

What is the Senior Manager, Data Engineering & Analytics role?

CopilotIQ is looking for a Senior Manager, Data Engineering & Analytics to lead and scale our data function. This is a remote, United States-based role reporting to the VP of Engineering.

This is a hands-on player-coach position. You will lead a small global team with two direct reports across the Americas and India, while personally contributing to data architecture, pipelines, analytics, dashboards, data quality, and customer-facing deliverables.

The ideal candidate is a strong data engineer first: resourceful, highly accountable, comfortable solving ambiguous problems, and able to communicate clearly with technical teams, business leaders, and customers.

You will own the foundation that supports clinical operations, product decisions, financial reporting, customer reporting, and company-wide analytics.

**
What you’ll own:**

• Lead and develop a small global team across data engineering, analytics, and BI.
• Own the architecture, reliability, and evolution of the analytical data platform.
• Design and build scalable batch and event-driven pipelines across clinical, operational, product, financial, and customer data.
• Establish strong data-quality practices, including testing, monitoring, lineage, reconciliation, alerting, and incident response.
• Define trusted metrics, dimensional models, curated datasets, and semantic layers.
• Deliver dashboards, recurring reports, customer reporting, self-service datasets, and actionable insights.
• Partner directly with clinical, operations, product, finance, engineering, and commercial stakeholders.
• Lead customer-facing discussions involving reporting requirements, metric definitions, discrepancies, and data-delivery issues.
• Investigate complex data problems, identify root causes, and implement durable solutions.