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Tier-1 brand, popular big-data title, and metro location increase applicant competition.
Requires banking risk data experience and specific Big Data stack, limiting cross-industry transferability.
Explicit 8+ years banking data engineering and mandatory Spark/NoSQL skills tighten filters.
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Define and manage target data architecture for risk information, ensuring alignment with Citi Data Standards.
Lead development and optimization of scalable, reliable Big Data engineering pipelines using Spark, Python, and NoSQL (HBase).
Prepare and present metrics, status reports, and analysis for senior management and support data quality and risk data integration initiatives.
8+ years of data engineering experience in Big Data ecosystem, with primary skills in Spark, Python, Spark Java, Scala, and SQL.
8+ years Banking or Financial Services experience with focus on risk management data structures and architecture.
Strong knowledge of NoSQL databases (HBase) and experience with data integration (ETL/ELT), migration, and large scale enterprise projects.
Bachelor's/University degree or equivalent experience.
Experienced in medium to large enterprise financial services projects involving Big Data and risk data architecture.
Able to influence, facilitate, and present results effectively to senior management.
Skilled in building scalable, maintainable data pipelines supporting batch, replication, and event streaming integration patterns.