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Popular data-engineer title, broad PySpark/AWS/Snowflake skillset, and Pune metro increase applicant competition.
Core data engineering skills (PySpark, SQL, cloud) are transferable across industries despite platform-specific tools.
Multiple mandatory tech stack requirements like PySpark, Snowflake, DBT, and AWS increase filter strictness.
Build and maintain data architectures including data pipelines, data warehouses, and data lakes ensuring data integrity, accessibility, and security.
Design and develop scalable data transformation and storage solutions using PySpark, Snowflake, and AWS Data Analytics stack (Glue, S3, Lambda, Lake formation, Athena).
Collaborate with data scientists to build and deploy machine learning models and lead or guide team members, managing operational processing and risk within own team.
Hands-on experience in PySpark including DataFrames, RDD, and SparkSQL.
Proven experience developing, testing, and maintaining applications on AWS Cloud, including AWS Data Analytics tools like Glue, S3, Lambda.
Experience with Snowflake for scalable data storage and transformation, including data ingestion of multiple storage formats (Parquet, Iceberg, JSON, CSV).
Work Experience Required: Should have worked on at least two major project implementations; Location Requirement: Based in Pune.
Strong technical expertise with cloud-based enterprise data warehousing and multiple data platforms including Snowflake and NoSQL environments.
Ability to engage stakeholders to elicit and translate requirements into ETL solutions and provide infrastructure-related solutions independently or collaboratively.
Experience leading or supervising teams, managing end-to-end operational accountability, embedding risk controls, and advising on data governance and lineage tools (preferred but not mandatory).