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High — Tier-1 brand, common Data Engineer title, metro location, and broad skill requirements increase competition.
High — requires specific banking/financial services experience and domain knowledge, limiting cross-industry transferability.
High — explicit 8+ years, mandatory Big Data stack (Spark, Databricks, Snowflake) and banking domain experience.
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Define and manage target data architecture for risk information, ensuring alignment with Citi Data Standards.
Develop and optimize scalable data pipelines, including data retention, cleanup, and consistency checks using Big Data technologies like Spark, NoSQL (HBase), and Python/Scala/SQL.
Lead data analysis and reporting efforts, prepare management reports for reviews, and support system migration programs including legacy-to-cloud transitions.
Minimum 8+ years of data engineering experience in the banking or financial services domain with focus on Big Data ecosystem and Spark.
Strong proficiency in Python, Spark (Java/Scala), SQL, and NoSQL databases (especially HBase).
Experience with data integration (ETL/ELT), building scalable data pipelines, and knowledge of Big Data cluster operations.
Bachelor’s degree or equivalent experience.
Experienced data engineer with deep knowledge of risk management data structures and financial services industry standards.
Demonstrated ability to influence and partner with cross-functional teams and senior management with strong communication skills.
Proven track record in handling medium to large enterprise projects involving complex data migration, integration, and analytics in financial services.