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Strong Tier-1 brand, popular mid-level data engineer role, and metro location increase candidate competition.
Core data engineering skills are transferable, though banking governance and controls add moderate domain bias.
Multiple mandatory technologies (PySpark, Snowflake, DBT, AWS) create strict technical filters for shortlisting.
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Build and maintain scalable data architectures including data pipelines, data warehouses, and data lakes ensuring data accuracy, accessibility, and security.
Design and implement data transformation and storage solutions using Snowflake and AWS Data Analytics stack (Glue, S3, Lambda, Lake Formation, Athena).
Collaborate with data scientists to build and deploy machine learning models; oversee technical delivery and guide team resources if in a leadership capacity.
Proven hands-on experience in PySpark (Dataframes, RDD, SparkSQL) and writing advanced SQL/PL SQL.
Strong experience with AWS Cloud services and AWS Data Analytics stack, along with Snowflake for data ingestion and ELT pipeline development using DBT.
Experience working on at least two major project implementations.
Location requirement: Pune; Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building cloud-based enterprise data warehouses involving Snowflake and NoSQL platforms, with strong understanding of data warehousing and data mart concepts.
Capable of engaging stakeholders to gather and translate requirements into ETL components, with ability to handle infrastructure-related solutions independently or collaboratively.
Demonstrated ability to lead or advise teams, escalate risks appropriately, and influence decision-making within their technical area of expertise.