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Tier-1 bank, mid-level metro data engineer role with common title and broad applicant pool.
Big-data and Java skills transferable, but market-risk and OLAP expertise favors finance background.
Mandatory 5+ years plus Spark/Java/OLAP and finance risk expertise increases filtering.
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Architect and build scalable data pipelines to process billions of trade-level Present Value calculations using Apache Spark.
Develop and optimize high-performance aggregation jobs and deliver intelligent data APIs for on-demand, flexible access to risk data.
Integrate NLP capabilities and build OLAP engine-based analytical tools to enable fast, intuitive data exploration and support senior stakeholders' decision making.
5+ years of experience with Core Java, Apache Spark, Big Data technologies, and Hive.
Expert-level programming skills in Java and/or Scala, plus advanced SQL expertise.
Experience with high-performance OLAP databases such as Apache Pinot, Apache Druid, or Trino.
Bachelor's degree in a quantitative or technical field (e.g., Computer Science, Financial Mathematics, Financial Engineering).
Proven ability to architect and deliver complex, large-scale data engineering solutions in financial services or similar environments.
Strong understanding of market risk analytics and risk calculations (VaR, Stress Testing, PV) to translate business needs into technical implementation.
Experience collaborating directly with senior business and quantitative stakeholders to design innovative data products.