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# Data Quality Engineer

- **Company:** IMC
- **Location:** Amsterdam, Netherlands
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Software Design Documents, Python (Programming Language), Production Code, Code Restructuring
- **Published:** September 4, 2026
- **Apply:** https://www.adzuna.nl/details/5869271633

## About the Role

* 3-5 years working with large-scale financial or scientific datasets.
 * A Master's degree in a quantitative discipline (Mathematics, Statistics, Econometrics, Physics, or similar).
 * Strong Python including the statistical libraries used for time-series analysis and surface fitting.
 * Comfortable moving between interactive analysis and production code.
 * A self-starter who finds the work that must be done, and does it without direction
 * Direct stakeholder communication with researchers and traders on data trust.
 * Intellectual curiosity about messy real-world market data.
 * Genuine interest in financial markets (no prior knowledge or experience is required)

## Description

At IMC, data shapes trading decisions across a wide range of financial products. The Data Quality team owns the integrity, completeness, and timeliness of the datasets our Quantitative Researchers and Traders rely on. As a Data Quality Engineer, you design the statistical methods that detect, characterise, and quantify issues in those datasets. Additionally, you own historical datasets that turn raw vendor and internal feeds into pricing-grade inputs and output.
Your responsibilities

Statistical methods

 * Design statistical methods from first principles to detect outliers, distribution shifts, stale values, missing values, and inconsistencies across market datasets.
 * Investigate anomalies, quantify their impact on pricing and trading decisions, and communicate findings through written analyses and design documents.
 * Refactor and generalise methods as new asset classes and regions expose edge cases.

Historical Datasets

 * Ingest, assess, clean and maintain historical data from various external sources by designing and owning automated pipelines for corrections
 * Engage researchers and traders directly on data trust, methodology, and trade-offs.
 * Coordinate across curators, quantitative researchers, and developers for each dataset.

Curation in production

 * Roll out new and existing methods to production across regions and asset classes.
 * Contribute production code in shared libraries


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