Job Description
We are a technology-led healthcare solutions provider. We are driven by our purpose to enable healthcare organizations to be future-ready. We offer accelerated, global growth opportunities for talent that’s bold, industrious, and nimble. With Indegene, you gain a unique career experience that celebrates entrepreneurship and is guided by passion, innovation, collaboration, and empathy. To explore exciting opportunities at the convergence of healthcare and technology, check out www.careers.indegene.com Looking to jump-start your career? We understand how important the first few years of your career are, which create the foundation of your entire professional journey. At Indegene, we promise you a differentiated career experience. You will not only work at the exciting intersection of healthcare and technology but also will be mentored by some of the most brilliant minds in the industry. We are offering a global fast-track career where you can grow along with Indegene’s high-speed growth. We are purpose-driven. We enable healthcare organizations to be future ready and our customer obsession is our driving force. We ensure that our customers achieve what they truly want. We are bold in our actions, nimble in our decision-making, and industrious in the way we work.
Role: Lead Data Engineer
Job Description:
You will be responsible for:
Design, develop, and maintain scalable data pipelines and enterprise data platforms
Lead architecture and implementation of modern data engineering solutions across cloud ecosystems
Build and optimize batch and real-time data ingestion frameworks
Design data models supporting analytics, reporting, and advanced analytics use cases
Drive data integration across multiple enterprise systems including healthcare and commercial data sources
Ensure data quality, governance, security, and compliance standards are implemented
Collaborate with Data Scientists, Analysts, Product Owners, and Business stakeholders to translate business requirements into scalable data solutions
Lead migration and modernization initiatives from legacy data platforms to cloud-based architectures
Optimize data processing performance and cost efficiency
Implement CI/CD practices and DataOps frameworks for data engineering workflows
Mentor and guide junior data engineers and establish engineering best practices
Support enterprise analytics, AI/ML, and reporting initiatives
Work within Agile delivery models and contribute to sprint planning and technical roadmap discussions
Must Have
Management Skills
Strong leadership and team mentoring capabilities
Excellent written and verbal communication skills
Ability to work with cross-functional global teams
Strong stakeholder management and solutioning mindset
Ability to drive technical decisions and architectural governance
Staying abreast of emerging technologies in cloud data engineering and analytics platforms
Your Impact
Enable scalable and future-ready enterprise data platforms
Improve data accessibility and reliability for analytics and business decision-making
Accelerate digital transformation initiatives across healthcare and life sciences clients
Establish engineering standards and best practices across data teams
About You: (Desired Profile)
Strong experience designing enterprise-scale data architectures
Experience working in cloud-native data ecosystems
Proven experience leading data engineering teams or large-scale implementations
Strong analytical and problem-solving mindset
Experience working in Agile environments
Understanding of healthcare or life sciences data ecosystem is a plus
Must Have: (Requirements)
8–12+ years of experience in Data Engineering
Strong expertise in Python / SQL / Spark
Hands-on experience with Cloud Platforms (AWS / Azure / GCP)
Experience with Data Warehousing solutions (Snowflake, BigQuery, Redshift, Synapse)
Experience building ETL / ELT pipelines
Distributed data processing frameworks (Apache Spark, Databricks)
Data orchestration tools (Airflow, ADF, or similar)
Strong understanding of data modeling concepts
Experience with large-scale structured and unstructured datasets
Version control and CI/CD practices
Good to have
Experience with Databricks Lakehouse architecture
Streaming technologies (Kafka, Kinesis, Pub/Sub)
Exposure to Healthcare / Life Sciences data domains
Experience supporting AI/ML pipelines
Containerization (Docker, Kubernetes)
Perks
Global exposure and career growth opportunities
Learning & development programs
Leadership mentoring initiatives
Flexible and collaborative work environment