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Tier-1 brand, metro location, and broad Big Data skillset raise candidate competition.
Requires 8+ years in banking risk data and Big Data ecosystem, limiting cross-industry transfers.
Explicit 8+ years and mandatory Big Data/Spark and banking risk requirements create strict filters.
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Define and manage target data architecture for risk information, ensuring alignment with other risk organizations and Citi Data Standards.
Develop, optimize, and oversee scalable data engineering pipelines, including data retention, cleanup, and consistency checks in NoSQL datastores such as HBase.
Prepare and present analysis, metrics, and reports for senior management and support data migration programs including legacy-to-cloud transitions.
8+ years of experience in Banking or Financial Services with strong data engineering background in Big Data ecosystem, primarily using Spark.
Proficiency in Python, Spark Java, Scala, SQL, and experience with ETL/ELT tools and large-scale data pipeline development.
Strong knowledge of NoSQL databases, especially HBase, and basic knowledge of Big Data cluster operations.
Bachelor’s degree or equivalent experience.
Experienced in analyzing and defining risk management data structures and architecture within financial services.
Demonstrated skills in influencing and facilitating cross-functional teams and presenting results to senior management.
Operates with a strong analytical and project management approach, capable of managing medium to large enterprise data projects and system migrations.