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Tier-1 brand, generalist Data Engineer title, broad Big Data tech stack, and likely metro location increase candidate competition.
Strong banking risk-data requirement and financial-services experience makes cross-industry transfer difficult.
Explicit 8+ years and mandatory Big Data, Spark, ETL, and NoSQL skills make filters strict.
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Define and manage target data architecture for risk information aligning with Citi Data Standards and coordinate with risk organizations and technology partners.
Develop, optimize and maintain scalable data pipelines and ETL/ELT processes leveraging Big Data technologies (Spark, NoSQL databases like HBase) to support risk data analysis and reporting.
Support project management activities including preparing presentations for senior management and performing data quality analysis and root cause investigations to identify improvements.
8+ years of experience in Banking or Financial Services with 8+ years in data engineering within Big Data ecosystems, primarily with Spark.
Strong proficiency in Python, Spark Java, Scala, SQL, and experience with data integration, ETL design, and NoSQL databases (HBase).
Bachelor’s/University degree or equivalent experience.
Experience with risk management data structures and architecture, and ability to present to senior management.
Experienced data engineer capable of influencing cross-functional teams and aligning data architecture with risk and compliance requirements in financial services.
Hands-on expertise in building and optimizing complex, scalable ETL pipelines and working with Big Data platforms, cloud migrations, and data quality frameworks.
Comfortable leading technical projects, coaching new recruits, and communicating findings clearly to senior stakeholders.