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Tier-1 brand and metro location increase applicant density, but senior specialization moderates volume.
Core data engineering skills are transferable, but seniority and financial-services governance increase domain specificity.
Explicit 12+ years plus mandatory dbt/Snowflake/AWS and data platform skills make filters strict.
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Lead design and development of reliable, scalable data and ML pipelines with comprehensive test coverage.
Integrate diverse data sources to build group data assets supporting analytics and business objectives.
Champion best practices, coding standards, and oversee implementation ensuring alignment to strategic roadmaps and risk management.
12+ years of experience in data engineering or related field with expert-level knowledge of tools like Git, Jenkins, dbt, SQL, PL/SQL, Hadoop, Snowflake.
Strong hands-on experience with AWS architecture and required AWS certification.
Bachelor’s or Master’s degree in Engineering (Information Technology).
Experience with source control, deployment tools, cloud data platforms, and monitoring tools such as Splunk and AppDynamics.
Experienced in financial services industry or similar complex domain environments.
Capable of independently managing multiple initiatives, engaging effectively with senior stakeholders and engineering teams.
Strong strategic planning ability focused on sustainable, reusable data assets and scalable migration programs from on-prem to cloud.