Senior Manager, Data Engineering – Analytics

🕒 August 25

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 13%

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CopilotIQ

WebsiteLinkedInAll Job Openings

51 - 200 employees

🏥 Healthcare

☁️ SaaS

🤖 Artificial Intelligence

💰 Series B on 2024-11

Healthcare • SaaS • Artificial Intelligence

CopilotIQ is an AI-driven healthcare company that provides a remote patient monitoring and clinical engagement platform for people with chronic conditions, primarily diabetes and hypertension. The company combines an AI-powered analytics engine (remoteIQ) with deep longitudinal data — biomarkers, social determinants of health, behavioral data, and medication adherence — to monitor members in real time, provide early warnings, and enable personalized interventions by US-based licensed nurses and clinical teams. CopilotIQ markets its solution to payors (including Medicare Advantage plans) and emphasizes measurable outcomes such as reductions in ER visits, hospitalizations, hypoglycemic and hyperglycemic events, medication reductions, and high retention and engagement metrics. The company also operates under HIPAA privacy practices and offers telemedicine/clinical services through affiliated medical entities.

📋 Description

• 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 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 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 • Build and operate pipelines using AWS Glue, Lambda, SNS, S3, PySpark, and Amazon Redshift • Develop and maintain dbt models, Airflow workflows, data tests, and monitoring • Design dimensional models, star schemas, and curated analytical layers • Use SQL and Python to investigate data, validate results, and solve production issues • Build and review dashboards and reports in Sigma, Looker, or similar BI tools • Translate ambiguous business and customer needs into scalable data solutions • Take business questions from discovery through metric definition, analysis, visualization, and recommendation • Review architecture, code, data models, dashboards, and analytical approaches • Communicate findings, risks, limitations, and recommendations to technical and non-technical audiences • Balance strategic platform improvements with urgent operational and customer needs

🎯 Requirements

• 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