Data Engineer

Role & responsibilities We are looking for a Data Engineer with strong exposure to capital markets and quantitative finance concepts , combined with handson experience in Python/R . The role involves building and maintaining data pipelines and analytical solutions and models to build indicies. Key Responsibilities Quantitative Analytics Support Work with quants and analysts to operationalise pricing, risk, and quantitative models using Python or R . Prepare, transform, and aggregate timeseries data (prices, curves, vol surfaces, factors) required for valuation and risk calculations. Support backtesting and performance analysis for trading and investment strategies. Capital Markets Very good understanding of the capital markets. Collaboration & Delivery Work closely with business stakeholders to understand requirements and deliver fitforpurpose solutions. Collaborate with platform/DevOps teams on performance, cost optimisation, security and access controls. Produce and maintain technical documentation for data models, pipelines and analytics jobs. Required Skills & Experience Experience Minimum 5 years of professional experience as a Data Engineer, Quantitative Analyst, or similar role in financial services . Practical exposure to capital markets . Technical Skills Strong programming experience in Python (preferred) and/or R for data processing, analysis and model implementation. Good knowledge of SQL for querying and optimising access to relational or cloud data stores. Familiarity with version control (Git) and basic CI/CD practices for data/analytics code. Quantitative & Domain Knowledge Understanding of core quantitative finance concepts such as timevalue of money, return and risk measures, probability distributions, and basic stochastic processes. Ability to work with timeseries financial data , yield curves, benchmarks and market indices. Nice to Have Experience with cloud platforms (Azure/AWS/GCP) and related data services (e.g. Data Lake, Data Factory, Synapse/BigQuery/Snowflake). Knowledge of statistics and machine learning as applied to financial data. Knowledge of Databricks Soft Skills Strong analytical and problemsolving skills with attention to detail and data quality. Ability to translate business and quantitative requirements into technical designs and implementations. Good communication skills to work effectively with the stake holders Proactive, ownershipdriven mindset with the ability to work in a fastpaced environment. Preferred candidate profile