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Tier-1 brand and desirable senior data engineering role increase applicant competition.
Finance-specific data governance and wealth-management domain increase background sensitivity and reduce transferability.
Strict years, deep big-data technology and leadership requirements will filter many applicants.
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Define and execute the data engineering roadmap focusing on Global Wealth Data to support portfolio managers and investment advisors.
Lead and mentor a globally distributed data engineering team, ensuring high performance and continuous improvement.
Oversee architecture and implementation of scalable data pipelines, warehouses, and lakes, ensuring data quality, integrity, and compliance with financial regulations.
10-15 years hands-on experience with big data technologies including Hadoop, Scala, Java, Spark, Hive, Kafka, Impala, Unix scripting.
4+ years experience with relational and NoSQL databases such as Oracle, MongoDB, HBase.
Strong proficiency in Python and Spark Java, with knowledge of ETL platforms (PySpark, DataStage, AbInitio), data modeling, and big data pipeline optimization.
Bachelor’s or equivalent degree in computer science, engineering, or related field.
Experienced in strategic leadership within data engineering for financial or wealth management data ecosystems.
Proven ability to manage and scale distributed technical teams with focus on collaboration and innovation.
Deep technical expertise in big data frameworks, cloud platforms (AWS, GCP, OpenShift), container technologies, and data governance for sensitive financial data.