Data Quality Engineer
At a glance
Mid-level Data Architecture & Governance role at IMC. Amsterdam.
Pay not stated Data Architecture & Governance salary
dataroles summary, based on the employer's posting.
What you'll do
- Build statistical checks for market datasets to spot outliers, shifts in distributions, stale or missing values, and inconsistencies
- Assess anomalies, measure their effects on pricing and trading decisions, then document your findings and methods
- Turn external and internal feeds into cleaned historical data through automated correction pipelines
- Adapt quality methods as asset classes and regions reveal edge cases, then deploy them across production datasets
- Work directly with researchers, traders, curators, and developers on data methods, trust, and trade-offs
What you bring
- Three to five years working with large financial or scientific datasets
- A master’s degree in a quantitative field such as mathematics, statistics, or econometrics
- Strong Python skills, including statistical tools for time-series work and surface fitting
- You can move from exploratory analysis into production code and speak directly with researchers and traders
Who this fits
This role suits someone who can investigate imperfect market data, work independently, and communicate clearly with researchers and traders. It is based in Amsterdam and focuses on engineering contributions to shared production libraries.
From the employer
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
What you bring to the team
- 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)
About Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.
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