Job Description
A leading quantitative-driven financial firm is looking for a Data Engineer to join their growing team. This is a highly impactful role where you will be responsible for building scalable, efficient, and reliable data pipelines that power core trading and research operations.
Key Responsibilities
Data Pipeline Development
- Design, develop, and maintain robust Python-based data ingestion pipelines for market data and internal sources.
- Build and manage a unified RPC-based data access library, compatible across research and trading systems.
- Own the lifecycle of new and existing datasets—acquisition, ingestion, validation, and integration.
Data Quality & Monitoring
- Implement automated validation checks for data consistency, completeness, and accuracy.
- Collaborate with researchers to troubleshoot data anomalies and establish data quality benchmarks.
- Vendor & Data Source Management
- Evaluate and onboard new data vendors (e.g., sentiment, factor models, fundamentals).
- Monitor usage and relevance of existing subscriptions to optimize costs and eliminate inefficiencies.
- Maintain a pipeline of exploratory and potential new data sources.
Collaboration & Documentation
- Partner with quant researchers and software teams to integrate data into models and tools.
- Write comprehensive documentation for datasets, processes, and libraries to enable efficient onboarding and collaboration.
Strategic Impact
- Contribute to the long-term evolution of the firm’s data stack.
- Stay up-to-date with trends in financial data, Python tooling, and infrastructure to bring best practices into the team.
- Assist in developing proprietary data signals and custom indicators (e.g., sentiment scores).
Key Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical discipline from a Tier-1 institution.
1–3 years of professional experience in data engineering, software development, or a Python-centric technical role.
Strong coding skills in Python, with experience in writing reusable functions, modules, and lightweight APIs.
Familiarity with libraries like pandas, numpy, and tools for time-series data manipulation. Understanding of version control (Git) and collaborative development practices.
Exposure to financial market data (e.g., equities, futures, derivatives) or previous experience in fintech or trading environments.
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