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Mid-level, popular Data Engineer role in Bangalore with generalist cloud and platform requirements drives medium competition.
Generic cloud data stack (Snowflake, Airflow, Python) is highly transferable across industries, so low sensitivity.
Explicit 6+ year requirement plus mandatory Snowflake, dbt, Airflow, and cloud skills implies medium strictness.
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Design, build, and maintain scalable, cloud-native data pipelines and products enabling analytics, reporting, compliance, and AI/ML initiatives.
Own end-to-end delivery lifecycle including development, testing, deployment, monitoring, and continuous improvement of data solutions.
Monitor production systems, resolve incidents within SLAs, and implement monitoring and automation to enhance platform reliability.
6–8+ years of experience in Data Engineering, Data Warehousing, or Data Platform Engineering.
Proven experience with enterprise-scale data solutions and cloud-based data platforms, specifically AWS.
Strong skills in SQL, data modeling, Python preferred (or Java, Scala, C# with willingness to transition to Python), Apache Airflow, dbt, and Snowflake.
Work Experience Required: 6–8+ years in relevant fields. Notice period: Not explicitly mentioned in the JD.
Experienced in designing and optimizing ELT/ETL workflows within complex cloud environments using modern tools like Apache Airflow, dbt, and Snowflake.
Technically proficient with software engineering best practices (Git workflows, CI/CD, code reviews) and automated deployment in Agile settings.
Able to independently manage end-to-end data pipeline delivery while collaborating closely with Data Architects, Product Owners, and multidisciplinary teams.