Senior Market Data Engineer
Overview
We are seeking a Senior Market Data Engineer to help build and enhance the data infrastructure supporting quantitative research and trading operations. This individual will play a key role in designing scalable data platforms capable of processing large volumes of real-time and historical market data. The ideal candidate will have deep experience in Python development, distributed data systems, cloud-based architectures, and financial data engineering.
Experience working with high-frequency market datasets, modern data lake technologies, and large-scale storage solutions is highly desirable.
Key Responsibilities
- Architect and develop scalable data ingestion and processing frameworks for financial market data.
- Build and maintain platforms that support the storage, transformation, and retrieval of large time-series datasets.
- Design reliable data pipelines capable of handling high data volumes with low latency and strong data quality controls.
- Optimize cloud-based compute and storage environments to support growing data demands.
- Implement efficient storage strategies leveraging columnar file formats and data lake architectures.
- Partner with quantitative researchers, traders, and engineering teams to deliver high-quality market data solutions.
- Develop monitoring, validation, and alerting processes to ensure data completeness and accuracy.
- Improve platform performance, scalability, and operational resiliency.
- Contribute to engineering standards, system documentation, and architecture reviews.
Qualifications
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field.
- 8+ years of professional software engineering or data engineering experience.
- Strong Python development skills and experience building production-grade data systems.
- Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP).
- Experience working with large-scale market data, time-series datasets, or real-time streaming platforms.
- Familiarity with containerized environments and orchestration technologies such as Kubernetes.
- Experience designing data pipelines and storage solutions for analytical workloads.
- Strong understanding of columnar storage formats such as Parquet.
- Exposure to modern table formats and data lake technologies, including Apache Iceberg, is a plus.
- Experience with time-series databases, market data platforms, or high-performance data services is preferred.
- Knowledge of financial markets, electronic trading, or quantitative research environments is advantageous.
- Excellent communication, collaboration, and problem-solving abilities.
Preferred Background
Candidates from electronic trading firms, quantitative investment firms, exchanges, market data providers, or other data-intensive financial environments will be particularly successful in this role.
FAQs
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