Risk Data Pipeline

About the Role

We are seeking a highly skilled Risk Data Pipeline Engineer to design, build, and maintain scalable data pipelines supporting risk management, market data, and trading analytics . The ideal candidate will have strong programming expertise, hands-on experience with data engineering workflows, and a deep understanding of financial instruments and risk concepts.

You’ll work closely with Risk, Quant, and Technology teams to ensure accurate, real-time data flow for risk computations, exposure monitoring, and regulatory reporting.

Key Responsibilities

Design, develop, and maintain end-to-end data pipelines for risk and P&L systems.

Integrate and transform data from multiple sources — trading systems, market data feeds, position data, reference data , etc.

Ensure data quality, consistency, and timeliness across all risk data layers.

Collaborate with Risk, Quant, and DevOps teams to support risk calculations and analytics.

Build automated validation, monitoring, and alerting systems for critical data flows.

Optimize ETL workflows and improve data processing efficiency and scalability .

Contribute to the design and implementation of data models and schemas for risk systems.

Troubleshoot data and performance issues in production environments.

Required Skills & Experience

Strong programming skills in Python and SQL (experience with Pandas , PySpark , or Airflow is a plus).

Hands-on experience with data pipeline tools (e.g., Airflow , Luigi , Kafka , AWS Glue , or similar).

Strong understanding of database systems — relational (PostgreSQL, MySQL, Oracle) and / or NoSQL (MongoDB, Redis).

Exposure to risk management concepts — VaR, exposure, sensitivities, stress testing, etc.

Familiarity with financial data types — trades, positions, reference data, pricing data, and market feeds.

Experience working in Unix / Linux environments with scripting knowledge (Shell, Bash).

Strong analytical and problem-solving skills, attention to detail, and ownership mindset.

Preferred / Nice-to-Have Skills

Knowledge of cloud platforms (AWS, GCP, or Azure) and data lake / data warehouse architectures.

Familiarity with quant / risk systems or risk engines (Murex, Calypso, proprietary risk systems).

Understanding of fixed income, derivatives, and equities .

Experience in distributed data processing (Spark, Hadoop).

Prior experience in hedge funds, investment banks, or fintech firms preferred.

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related discipline .

Strong academic background with exposure to data structures, algorithms, and statistics .

Why Join Us

Work with global Risk, Quant, and Technology teams on mission-critical systems.

Be part of a high-performance engineering culture driving real-time financial insights.

Opportunity to shape and optimize data-driven risk infrastructure in a dynamic financial environment.