Manager Data Platform Engineer at HealthEdge in IN-Bangalore

Job description

• Data Engineering Manager
• Data Platform Team | Bangalore , India
• About HealthEdge
• We are looking for an experienced and passionate Data Engineering Manager to lead and grow the Data Platform team based in Bangalore . In this role, you will own the strategy, architecture and execution of our enterprise data platform, enabling reliable, scalable and secure data pipelines that power analytics, reporting and AI/ML initiatives across the organization. You will manage a team of data engineers while collaborating closely with product, engineering and other stakeholders to deliver high-impact data solutions.

Responsibilities

• Lead, mentor and grow a high-performing team of data engineers fostering a culture of technical excellence, collaboration and continuous improvement.
• Define and execute the technical roadmap for the data platform, aligning with broader engineering and product strategy.
• Design and build scalable, fault-tolerant data pipelines and ETL/ELT frameworks that support batch and real-time data processing at enterprise scale.
• Architect and evolve the data lakehouse , data warehouse and data integration layer using modern cloud-native technologies (e.g., AWS, Snowflake, Spark, Kafka, Airflow, dbt ).
• Establish and enforce data governance, data quality and data security standards across all platform components.
• Partner with cross-functional stakeholders – including product management, software engineering, analytics and data science teams – to understand data needs and deliver solutions.
• Drive adoption of DataOps and engineering best practices including CI/CD for data, automated testing, monitoring, alerting and documentation.
• Manage sprint planning, backlog prioritization and delivery cadence for the data platform team.
• Evaluate, recommend and implement new tools, frameworks and technologies to keep the data platform current and competitive.
• Collaborate with leadership in Boston and Bangalore to align on priorities, timelines and resource planning.
• Recruit, hire, and develop a talented team of data and machine learning engineers. Mentor and coach team members to foster professional growth and innovation.

Requirements

• Required
• Bachelor’s or Master’s degree in Computer Science , Engineering, Data Science or a related field.
• 10+ years of hands-on experience in data engineering, with at least 5 years in a people management role leading data platform or data infrastructure teams.
• Deep expertise in data warehousing, data modeling (dimensional and Data Vault), ETL/ELT design and big data technologies.
• Strong proficiency with cloud data platforms such as AWS (Redshift, Glue, S3, EMR, Lambda), Snowflake or Databricks.
• Experience with orchestration tools (Apache Airflow, Prefect or similar), streaming platforms (Kafka, Kinesis) and transformation frameworks ( dbt , Spark).
• Solid understanding of data governance, metadata management, data cataloging and data quality frameworks.
• Familiarity with BI & dashboarding tools and multi-dimensional modeling
• Great problem-solving capabilities, troubleshooting data issues and experience in stabilizing big data systems
• Excellent communication skills with the ability to translate technical concepts for non-technical stakeholders.
• Demonstrated ability to hire, develop and retain top engineering talent.
• Experience in the healthcare or health insurance domain, with familiarity with claims, enrollment and care management data.
• Hands-on experience with ML feature stores, data mesh architectures or real-time analytics platforms.
• Knowledge of HIPAA, PHI handling requirements and healthcare data security standards.
• Experience working in a globally distributed engineering organization.
• Familiarity with infrastructure-as-code (Terraform, CloudFormation) and containerized environments (Docker, Kubernetes).

Skills

Required

• Data warehousing
• ETL/ELT design
• Big data technologies
• Streaming platforms: Kafka, Kinesis
• Transformation frameworks: dbt, Spark
• Data governance
• Metadata management
• Data cataloging
• Data quality frameworks
• BI & dashboarding tools
• Multi-dimensional modeling
• Problem-solving

Preferred

• ML feature stores
• Data mesh architectures
• Real-time analytics platforms
• HIPAA, PHI handling requirements
• Healthcare data security standards