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Tier-1 bank, metro location, generalist developer title and broad big-data skillset create high competition.
Big-data and OLAP skills transfer across industries, but finance risk domain preference raises sensitivity to medium.
Explicit 8–12 years plus required Spark, OLAP and high-performance data engineering skills make screening highly strict.
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Architect and build scalable data pipelines processing billions of trade-level Present Value calculations for market risk.
Develop and optimize Apache Spark-based aggregation jobs and deliver intelligent APIs providing on-demand risk data access.
Integrate NLP-driven query interfaces and load data into high-performance OLAP engines to enable fast, interactive analytics for senior stakeholders.
8-12 years of experience in software development with strong programming skills in Python, Java, Scala, and expert-level SQL.
Hands-on expert experience with big data technologies, especially Apache Spark, and high-performance OLAP databases like Apache Pinot, Apache Druid, or Trino.
Degree in quantitative or technical field such as Computer Science, Financial Mathematics, or Financial Engineering.
Work Experience Required: 8-12 years; Notice Period: Not explicitly mentioned in the JD.
Experienced technologist with deep expertise in building large-scale data pipelines and analytics platforms in financial market risk environments.
Comfortable interfacing with senior quantitative and risk stakeholders to translate complex analytics needs into technical solutions.
Demonstrates strong fundamental computer science skills and applies AI-first principles with interest or experience in NLP integration for data analytics.