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High due to Tier-1 brand, generalist data engineer title, broad required skillset, and likely metro hiring.
Medium because core data engineering skills transfer across industries, but capital markets and security requirements increase specialization.
High due to VP-level seniority, mandatory large-scale data platform experience, Python/Spark/Postgres, and regulated financial environment.
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Design and build the data architecture and production-grade Python data pipelines supporting generative AI products across financial Markets, handling billions of records.
Create curated, low-latency data-serving layers enabling AI applications, including data ingestion, validation, transformation, enrichment, and integration from multiple internal and external sources.
Shape platform architecture, engineering standards, data models, and operational controls such as data quality, lineage, and security to ensure reliability and compliance in production systems.
Extensive hands-on experience in data or software engineering with practical Python expertise.
Strong SQL skills with experience in relational databases, particularly PostgreSQL.
Experience designing and delivering large-scale data platforms or data-intensive applications that process vast datasets.
Work Experience Required: Not explicitly mentioned in the JD.
Senior-level engineer comfortable owning end-to-end data platform architecture and delivery for AI products in financial Markets.
Experienced in building scalable, high-performance data pipelines using Python and managing complex datasets with strong data governance and security considerations.
Able to operate in ambiguity with strong technical judgment, influencing product and engineering standards from early-stage platform design.