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 indices.

Key Responsibilities

• Quantitative Analytics Support

• Work with quants and analysts to operationalize 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 optimization, 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 optimizing 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