Software Engineer - Data Engineering

About You – Experience, Education And Skills

• Bachelor’s degree in Computer Science, Engineering, or related field.
• (0-2) years of experience building scalable, production-grade data systems.
• Proven ability to design massively scalable distributed data processing pipelines.
• Strong background in database design, schema modeling, and performance tuning.
• Hands-on expertise building and optimizing complex ETL/ELT pipelines that power ML and analytics workloads.
• Experience designing resilient, fault-tolerant, cloud-native data platforms with automated disaster recovery.
• Hands-on background in Agile delivery, CI/CD, and containerized workflows.
• Strong understanding of data versioning, lineage, reproducibility, and metadata management — critical for AI governance.
• Proficient in Python, SQL, and PySpark
• Bonus: experience building data prep scripts for ML model training
• Strong experience with AWS: EMR, Glue, S3, EC2, RDS, Aurora PostgreSQL, Lambda
• Ability to evaluate and integrate AI-friendly tools (feature stores, vector databases, ML workflow orchestration, etc.)
  

It would be great if you also have

• Exposure to GenAI technologies, LLM data pipelines, or vector embeddings
• Experience supporting data needs for ML, LLMs, or analytics teams
• Experience collaborating with distributed, high-velocity global teams
• Should have knowledge and experience in healthcare domain.
• Good understanding in REST API .
• Understanding of Cloud and Redshift
  

What you will be doing in this role?

• Works as a data engineer and end to end ETL pipeline.
• Writes quality, well-tested, documented code.
• Adheres to development best practices and standards as set within the team.
• Helps support existing systems, diagnosing issues and identifying bugs.
• Contributes to the overall development of the platform, keeping the ‘bigger picture’ in mind.
• Works closely with QA, DevOps, Product Owners, Business Analysts and Project Management.
• Adheres to Agile development practices and aid the team in doing so
  

About The Team

Hours of Work

• Full-time (IST)
• 40 hours per week
• Hybrid working environment