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Tier-1 brand, metro location, and broad data engineering skillset increase candidate competition.
Role demands deep quantitative finance and data engineering expertise, limiting cross-industry transferability.
Explicit 7+ years plus mandatory Scala, Spark, Snowflake/Databricks and finance domain requirements create highly strict filters.
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Own development and enhancement of advanced quantitative data engineering solutions supporting portfolio risk analytics and investment intelligence.
Leverage Big Data technologies including Spark, Snowflake, Databricks to build scalable analytics pipelines and production workflows for diverse financial datasets.
Collaborate with investment teams and clients to deliver trusted data products and AI-enabled automation for analytics across structured products, credit, private markets, energy, and climate models.
7+ years of experience in Data Engineering, Quantitative Analytics, or Financial Technology environments.
Strong programming expertise in Scala, with solid knowledge of functional programming and distributed computing frameworks like Spark.
Hands-on experience with enterprise data platforms such as Snowflake, Cassandra, and cloud solutions including Databricks or Dataproc.
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Economics, Statistics, or a related quantitative discipline.
Deep understanding of portfolio risk analytics data and financial instruments including Bonds, Derivatives, and Credit Products.
Experienced in building scalable data products using modern Big Data technologies integrated with AI/ML workflows for operational efficiency.
Capable of working across quantitative, technology, and investment teams to translate complex financial data needs into production-ready analytical solutions.