Manager, Data Engineering

As the Manager of Data Engineering, you will lead a team of data engineers in building scalable, secure, and high-performance data pipelines and infrastructure. You’ll partner cross-functionally with product, engineering, and business stakeholders to define and execute a data engineering roadmap that supports DexCare’s internal analytics and external product capabilities. This role blends strategic leadership with hands-on technical execution in a highly regulated healthcare environment.

This is not a hands-off role: the right candidate has deep enough data engineering experience to engage meaningfully in technical discussions, code reviews, and architectural trade-offs, while knowing when to step back and let engineers lead.

This manager owns the health of the team as much as the output. Sprint planning, 1:1s, performance reviews, hiring, and career development are just as important as shipping pipelines on time. The role operates at the intersection of engineering delivery and people leadership — balancing short-term execution (sprint goals, incident response, stakeholder SLAs) with long-term investment (mentorship, technical growth, team culture, and process improvement).

• Build, mentor, and manage a high-performing team of data engineers.
• Define and execute the data engineering roadmap aligned with DexCare’s strategic goals.
• Architect and oversee scalable data pipelines for structured, semi-structured, and unstructured data.
• Collaborate with product and engineering teams to deliver cross-functional data solutions.
• Own the end-to-end lifecycle of data ingestion, transformation, storage, and delivery.
• Ensure data quality, governance, and compliance with HIPAA and HITRUST standards.
• Manage relationships with third-party data vendors and integration partners.
• Drive adoption of modern data engineering practices including CI/CD, Infrastructure as Code, and observability.
• Communicate effectively across technical and non-technical audiences, including executives.

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 [CM1]It should be execute. the definition is coming from us

• Bachelor’s degree in Computer Science, Engineering, or related field.
• 7+ years of experience in data engineering, with 2+ years in a leadership role.
• Proven experience designing and managing large-scale data pipelines and lakehouse architectures.
• Expertise in Spark, Flink, dbt, and orchestration tools like Airflow or Prefect.
• Strong proficiency in Python, Scala, SQL, and NoSQL databases.
• Experience with cloud platforms (AWS/GCP), Databricks, and data warehouses (Snowflake, BigQuery, Redshift).
• Familiarity with visualization tools (Tableau, Power BI, D3).
• Experience with CI/CD pipelines, version control (Git), and production-grade software systems.
• Strong architectural skills and ability to resolve performance bottlenecks.
• Excellent leadership, communication, and stakeholder management skills.

• Experience managing real-time data pipelines with low-latency SLAs in healthcare domain
• Hands-on experience with Airflow and ML model deployment in cloud environments.
• Exposure to deep learning, reinforcement learning, or NLP in production settings.
• Experience in healthcare data environments and regulatory compliance.