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Tier-1 brand, metro location, mid-level generalist data role with broad skillset drives high competition.
Financial risk and treasury domain exposure increases domain specificity, though core data engineering skills transfer.
Multiple mandatory technical skills and 4+ years experience enforce strict shortlisting.
Design, develop, optimize, and maintain scalable, metadata-driven data engineering frameworks supporting Corporate Treasury financial risk processes.
Lead and deliver moderately complex data engineering initiatives including building resilient data pipelines, APIs, and supporting components across cloud and on-prem environments.
Play a key role in Data Center exit migrations, DPC onboarding, and enterprise-wide modernization involving distributed data engineering, cloud platforms, and data quality improvements.
Minimum 4+ years of software engineering experience or equivalent demonstrated via work experience, training, education, or military.
Hands-on experience with Python, SQL, and bash scripting for automation mandatory.
Experience building big data pipelines using Apache Spark, Hive, Hadoop required.
Work experience: 4+ years; Notice period: Not explicitly mentioned in the JD.
Experienced in designing and optimizing large-scale structured and unstructured data pipelines using modern lake/lakehouse technologies (Spark, Iceberg, Delta).
Proficient with cloud-native engineering practices including serverless architectures, containerization (Docker, Kubernetes), and CI/CD pipelines.
Familiarity with data quality frameworks, financial domain expertise (risk, treasury, ALM), and ability to lead technical initiatives independently in an enterprise financial services environment.