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Strong Barclays brand, popular Data Engineer title, and Pune metro location drive high applicant competition.
Core data engineering skills transferable but banking context adds moderate domain sensitivity.
Multiple mandatory technical stack requirements and regulated banking environment increase screening strictness.
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Build and maintain robust data pipelines, data warehouses, and data lakes ensuring data accuracy, accessibility, and security.
Design scalable data architectures using technologies including PySpark, Snowflake, and AWS data analytics stack to support data processing and analysis.
Collaborate with data scientists to build and deploy machine learning models and support Location Strategy project deliveries within quality and governance standards.
Hands-on experience with PySpark, including Dataframes, RDD, and SparkSQL.
Proven development and maintenance experience on AWS Cloud, specifically with Glue, S3, Lambda, Lake formation, Athena.
Experience in Snowflake for data transformation/storage and ELT pipeline development using DBT.
Work Experience Required: Minimum two major project implementations involving data engineering technologies.
Strong technical expertise in cloud-based data engineering focusing on AWS and Snowflake ecosystems.
Experience working on Enterprise Data Warehouse implementations and knowledge of Data Warehousing and Data Mart concepts.
Capability to engage with stakeholders to translate requirements into ETL components and provide infrastructure solutions collaboratively.