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Tier-1 brand plus cloud-data role attracts candidates, but senior level reduces applicant density.
Core cloud data engineering skills transfer across industries, but financial-services preference increases domain specificity.
Explicit 10-15 years and mandatory Databricks/AWS/Python/SQL requirements enforce strict shortlisting filters.
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Design and develop high volume data ingestion solutions and manage cloud data lake tooling, primarily on AWS with Databricks.
Develop and maintain cloud infrastructure, data pipelines (ETL), and integrations between on-premises and cloud systems to support business needs.
Own technical design, build, testing, deployment, production support, and adherence to coding, privacy, and security standards.
10-15 years of work experience in data engineering, with hands-on cloud data engineering experience.
Expertise in AWS Cloud Data Engineering, Databricks platform, Python, Pyspark, SQL coding, Oracle RDBMS, and data modeling/database design.
Strong knowledge of cloud security in AWS/Azure (IAM Roles & Policies, Security Groups, Encryption keys).
Experience with performance tuning, source control (GIT), CI/CD tools (Jenkins), and job schedulers (Autosys/Control-M).
Experienced in building and managing cloud-based database engineering platforms, preferably in Financial Services domain.
Capable of hands-on development within a fast-paced, small elite development team.
Strong operational ownership of end-to-end cloud data engineering solutions including security, deployment, and production issue resolution.