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Metro Bangalore, generalist data-engineer title and broad skillset create high applicant density.
Data engineering skills (SQL, Airflow, cloud) are highly transferable across industries.
Explicit 1–3 year requirement plus mandatory SQL, Python, and cloud/data stack drive moderate strictness.
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Build and maintain scalable batch ETL/ELT data pipelines for ingesting, transforming, and orchestrating complex data workflows.
Implement data quality checks and debug pipeline failures to ensure reliable, maintainable data solutions.
Collaborate with cross-functional teams to deliver data requirements and optimize data models using fact and dimension tables in cloud data platforms like Snowflake, BigQuery, or Redshift.
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or related technical field.
1–3 years of professional experience in Software Engineering, Data Engineering, or a closely related role.
Proficiency in Python preferred, strong SQL skills, and working knowledge of orchestration tools (e.g., Apache Airflow, dbt, Azure Data Factory).
Exposure to cloud platforms (AWS, GCP, or Azure) and cloud data warehouses; experience with ETL/ELT concepts and data pipeline workflows.
Experience operating in data engineering environments building and optimizing ETL/ELT pipelines in cloud-native architectures.
Comfortable troubleshooting data pipeline issues using debugging and monitoring tools and improving pipeline reliability and scalability.
Demonstrated collaboration with data scientists, AI researchers, and product teams to deliver production-quality data platforms within AI-driven healthcare or related domains.