Lead Python Engineer - Big Data & Risk Analytics

Job Title: Lead Python Engineer – Big Data & Risk Analytics

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Location: Jersey City, NJ

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Duration: Contract - 6 months (C2H potential)

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Job Description

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We are seeking a Lead Engineer, Big Data Risk Aggregation to join our dynamic team. The ideal candidate will have strong experience in large-scale data engineering, Apache Spark, and risk aggregation within Capital Markets and a proven ability to design and operate high-performance, auditable aggregation platforms that deliver reportable risk metrics.

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Responsibilities:

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• Architect and build a high-performance, metadata-driven aggregation platform using Apache Spark.
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• Design and implement algorithms to reduce computational complexity for high-dimensional risk aggregations.
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• Create and maintain a semantic layer and business-friendly data model to power BI and AI-driven queries.
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• Optimize Spark internals including partitioning, shuffle strategies, and memory for extreme performance.
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• Build a self-service framework that enables quants and risk managers to define new aggregations without new code.
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• Deliver high-performance APIs and curated datasets for UIs and Python Notebooks to support analysis and replication.
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• Load aggregated metrics into OLAP stores such as Apache Pinot or Apache Druid to support analytics and UI layers.
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Required Skills & Qualifications:

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• 10+ years of hands-on large-scale data engineering experience, preferably in Capital Markets or Risk technology.
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• Expert-level Apache Spark knowledge including architecture, performance tuning, shuffle optimization, and partitioning.
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• Deep understanding of risk aggregation for VaR, CCAR, Stress Testing, and FRTB.
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• Strong programming skills in Java and/or Python with expert-level SQL.
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• Experience building semantic layers and data models enabling BI and AI applications.
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• Proven delivery with high-performance OLAP databases such as Apache Pinot or Apache Druid.
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• Degree in Computer Science, Financial Mathematics, Financial Engineering, or a related quantitative field.
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Preferred Skills:

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• Experience leading data engineering teams and establishing best practices for big data processing.
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• Demonstrated strength in performance diagnostics across CPU, memory, and network I/O.
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