Senior Data Analyst - Trading

Orbis
Charing Cross, United Kingdom
12 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
£ 140K

Job location

Charing Cross, United Kingdom

Tech stack

Information Engineering
ETL
Python
NumPy
DataOps
SQL Databases
Technical Data Management Systems
Data Ingestion
Pandas
Data Pipelines

Job description

Senior Data Analyst - Python (Pandas/Numpy) - Trading Orbis are partnering with a global Commodities Trading firm who are investing heavily in data-driven decision-making, and embracing modern technology across the business, including GenAI. As part of this growth, they're looking for a Senior Data Analyst with strong python and ETL/ELT experience to join their data team and ensure commercial teams have the data they need, when they need it. This is an incredible opportunity to work at the intersection of data, technology, and trading, supporting the global commercial teams and shaping how data underpins market insights, pricing models, and investment decisions. In this role, you will: Design, build, and optimise data pipelines and market data platforms in Python for scalable, high-performance analytics Manage end-to-end data ingestion (ETL), transformation, and quality control, ensuring data accuracy and reliability Collaborate with traders, quants, and data scientists to translate

Requirements

commercial needs into technical data solutions To be successful in this role, you will need: 4+ years' experience in Data Operations, Data Engineering, or similar, within a Trading/Investment firm, or a firm providing services to Trading or Investment firms (Essential) Excellent Python (Pandas, NumPy) and SQL experience Excellent ETL experience managing the full end-to-end process from Data Ingestion through to Data Publishing and maintenance of data pipelines If you feel your experience is a match with the above, please apply with an up to date copy of your CV in the first instance.

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