At IMC, data is pivotal in shaping trading decisions across a wide range of financial products. Better data means better decision making for both the research and trading teams. We’re on a mission to ensure that we have access to quality data to enhance our ability to rapidly adapt and innovate.

This role is ideal for someone passionate about financial data, quantitative research, and market analysis. As a Market Data Quality Analyst, you’ll own a variety of reference data, corporate actions data and alternative data from sources such as exchanges and data vendors. You’ll then work to turn these into research and trading-ready datasets via ingestion, validation, mapping, enrichment and publication.

Your Core Responsibilities:

• Acquire, clean, and maintain reference data, corporate actions data and alternative data from various sources (exchanges, third-party vendors, proprietary feeds).
• Design and own automated Python pipelines for ingestion, transformation and publication of these sources.
• Analyse datasets using SQL, Pandas and Polars to help model, investigate issues, and document data.
• Monitor and resolve data anomalies, missing values, and inconsistencies to ensure high data quality and develop automated testing strategies to flag such issues.
• Work with engineers to enhance data storage, retrieval, and efficiency for research workflows.
• Work with researchers, traders and other data users to turn data needs into well-defined, maintained datasets.

Your Skills and Experience:

• 5+ years in a data-focused role, preferably in a trading, quantitative finance, or financial services environment.
• Strong Python including data processing frameworks such as Pandas or Polars.
• Advanced SQL for analysing large datasets efficiently.
• Genuine interest in how exchanges work – including instrument lifecycles, session structures, holiday calendars and symbology.
• Eagerness to get into the details of a dataset, either to find quality issues or to help document data edge cases.
• Strong communication skills and the ability to work cross-functionally with researchers, traders and engineers to build up data requirements.
• Nice to have: visualisation tools (Streamlit, Plotly), distributed processing (Spark, Dask), CI/CD, Kubernetes.