Senior Data Engineer – Trading & Operation

asset-management, technology, Data & BI

Perm

Hybrid

The Role

This is a senior data engineering role embedded within a trading-focused environment, supporting execution, operations research, and quantitative trading teams.

You’ll help build and maintain the data platform that underpins trading analytics across multiple asset classes. The work is practical and production-oriented — ensuring data is reliable, accessible, and fit for purpose in time-sensitive trading workflows.

This role suits someone who enjoys working close to trading and research teams while owning core data infrastructure.

What You’ll Be Working On

• Designing and maintaining scalable data pipelines from multiple sources

• Improving reliability and monitoring of data ingestion processes

• Transforming raw data into analytics-ready datasets

• Automating data and analytics workflows where possible

• Implementing data quality checks and pipeline observability

• Partnering with quantitative trading teams to enhance platform performance

• Supporting data used across execution, operations, and research

What They’re Looking For

• Strong experience in data engineering or data-intensive environments

• Hands-on programming experience in Python or similar languages

• Experience working with high-volume, structured and unstructured datasets

• Understanding of data quality, monitoring, and automation concepts

• Exposure to trading data, market structure, or transaction cost analysis is a plus

• Comfortable working independently and owning initiatives

• Clear communicator who works well with technical and non-technical partners

Background

• Degree in computer science, engineering, or a related technical discipline

• Several years of relevant industry experience

• Track record of improving data platforms in production environments

Why This Role

• Direct alignment with trading and execution teams

• Ownership over data used in live decision-making

• Opportunity to improve systems that operate at scale

• Hybrid working within a collaborative, research-driven culture

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