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Tier-1 brand, metro location, popular Data Engineer title, and broad cloud data skillset increase candidate competition.
Core data engineering skills are transferable but financial services governance and MDM needs increase domain specificity.
Explicit 8+ years plus mandatory cloud data stack, ETL, governance, and CI/CD requirements make filters stringent.
Design, develop, and support end-to-end scalable data pipelines on AWS Cloud using platforms like Redshift, Databricks, and Snowflake.
Implement and optimize data ingestion, transformation, validation frameworks, and data warehousing solutions ensuring performance, reliability, and data quality.
Collaborate with architects, application teams, and product owners to define data strategies, deliver data models, and support production stability including incident management and compliance with data governance.
8+ years of experience in data engineering, ETL, data ingestion, data warehousing, and analytics.
Hands-on experience with cloud data platforms: AWS, Redshift, Databricks, Snowflake, Spark, and related technologies.
Proficiency in Python, SQL, Shell scripting, and experience with CI/CD tools such as GIT, Harness, JFrog, and JIRA for agile delivery.
Experience in data quality frameworks, automated data validation, production support, and compliance with data security and SDLC standards.
Experienced senior professional capable of owning end-to-end data pipeline architecture and operational support in a cloud environment.
Skilled at working cross-functionally with architecture, product owners, and analytics teams to translate business requirements into technical data solutions.
Comfortable with agile delivery practices, incident management, and driving continuous improvement in automation and platform capabilities.