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Popular Data Engineer title, metro Bangalore location, mid-level role, and broad Snowflake/DBT/AWS requirements raise competition.
Core data engineering skills are highly transferable across industries; banking experience is listed only as a plus.
Mandatory Snowflake, DBT, AWS, Airflow, SQL, Python, and data platform experience imply strict technical filtering.
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Design and build data ingestion pipelines and maintain enterprise-scale data platforms including Data Warehouse, Data Lakes using Snowflake and AWS.
Create reusable data pipelines/frameworks with Python and optimize performance of data pipelines and SQL queries.
Collaborate with global teams, support production incidents, and ensure data security, privacy, and governance compliance.
Experience building data warehouse and data lake solutions with Snowflake and AWS cloud environment.
Proficiency in ELT tools such as DBT and AWS Glue, along with SQL and Python scripting.
Familiarity with data pipeline orchestration tools like Airflow and experience in GitLab CI/CD processes.
Work Experience Required: Not explicitly mentioned in the JD.
Strong technical expertise in enterprise data engineering focusing on high-volume ETL/ELT processes and data modeling (logical and physical).
Experience working within Agile frameworks and collaborating with cross-functional, global teams predominantly in US time zones.
Prior experience in financial industry or related domain is a plus but not mandatory.