Senior Manager, Data Engineering & Analytics

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. Improve platform performance, cost efficiency, security, privacy, and maintainability. Set priorities, review technical work, coach team members, and help scale the organization as the company grows. What You’ll Be Doing Building and operating pipelines using AWS Glue, Lambda, SNS, S3, PySpark, and Amazon Redshift. Developing and maintaining dbt models, Airflow workflows, data tests, and monitoring. Designing dimensional models, star schemas, and curated analytical layers. Using SQL and Python to investigate data, validate results, and solve production issues. Building and reviewing dashboards and reports in Sigma, Looker, or similar BI tools. Translating ambiguous business and customer needs into clear, scalable data solutions. Taking business questions from discovery through metric definition, analysis, visualization, and recommendation. Reviewing architecture, code, data models, dashboards, and analytical approaches. Communicating findings, risks, limitations, and recommendations to technical and non-technical audiences. Balancing strategic platform improvements with urgent operational and customer needs. What you'll bring: Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience. 5+ years of data engineering / data-platform experience. 2+ years of technical leadership experience and mentoring engineers. Deep hands-on experience designing, building, and operating production data platforms and pipelines. Strong experience with data architecture, ingestion, orchestration, transformation, modeling, warehousing, and performance optimization. Advanced SQL skills and strong proficiency in Python and PySpark. Experience with dbt, Apache Airflow, AWS Glue, or comparable tools. Experience designing dimensional models, star schemas, and curated analytical layers. Demonstrated ownership of data quality and reliability, including testing, monitoring, lineage, reconciliation, and operational support. Experience building dashboards, reports, semantic layers, and self-service datasets using Sigma, Looker, or comparable platforms. Strong backend engineering fundamentals, including APIs, distributed systems, and event-driven architecture. Experience working directly with customers, executives, and cross-functional stakeholders. Excellent written and verbal communication skills. Strong ownership, urgency, judgment, resourcefulness, and follow-through. A hands-on leadership style and willingness to personally solve difficult problems. Ability to lead effectively across time zones. Technologies Cloud and Data Platform: AWS, Amazon Redshift, S3, Lambda, Glue, SNS Transformation and Orchestration: dbt, Apache Airflow, AWS Glue Processing: Apache Spark, PySpark Databases: DocumentDB, MongoDB, DynamoDB, or similar operational databases Analytics and BI: Sigma, Looker, or comparable platforms Languages: Advanced SQL, Python, PySpark; Java or another backend language is helpful Modeling: Dimensional modeling, entity-relationship modeling, star schemas, and semantic layers Bonus Points Experience with healthcare data, payer data, clinical workflows, remote patient monitoring, or care-management operations. Working knowledge of HIPAA and secure handling of protected health information. Experience producing customer-facing healthcare or operational reporting. Experience leading a distributed or global team. Experience scaling a data function or hiring data engineering and analytics talent. Familiarity with Terraform or infrastructure as code. Experience with data governance, metric definitions, data catalogs, or semantic-layer initiatives. Familiarity with machine-learning techniques or platforms.