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Metro Bangalore, popular mid-level data engineer title, and broad generalist requirements increase applicant competition.
Core data engineering skills are transferable, but healthcare domain exposure adds moderate bias.
Explicit 1–3 years plus required Python, SQL, and Airflow skills make filtering moderately strict.
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Develop and maintain scalable batch ETL/ELT data pipelines for ingestion, transformation, and orchestration supporting AI-driven healthcare products.
Implement data quality checks and debugging to ensure reliability, including schema validation, null checks, deduplication, and pipeline failure resolution.
Collaborate with cross-functional teams including data scientists and product teams to fulfill data requirements using cloud data platforms and modern data engineering tools.
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or related technical discipline.
1–3 years of professional experience in Software Engineering, Data Engineering, or related role.
Proficiency in Python (preferred) and strong SQL skills.
Experience or working knowledge of orchestration tools (Apache Airflow, dbt, Azure Data Factory) and cloud platforms (AWS, GCP, or Azure).
Experienced in building and optimizing data pipelines in cloud environments using modern data engineering tools and cloud-native warehouses like Snowflake, BigQuery, or Amazon Redshift.
Capable of operational ownership including troubleshooting data pipeline issues and improving pipeline reliability and scalability.
Comfortable collaborating across multidisciplinary teams (software engineers, data scientists, AI researchers) in a fast-paced, innovation-driven setting focused on healthcare technology.