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Data & AnalyticsNot provided by source

Data Quality Engineer

IMC · Not provided by source

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Last seen by MeritLog September 11, 2026Source: GreenhouseSource version: greenhouse-job-board-v1

MeritLog read this listing from IMC's Greenhouse job board and last checked it on September 11, 2026.

Source: the employer's Greenhouse job board. Open the original listing for current details.

Job details

Work model
Not provided by source
Salary
Not listed by source
Location
Amsterdam, Netherlands

Hiring context

How this role compares at IMC

IMC has 170 live roles in MeritLog’s catalog across 9 job families, and 101 of them are in data & analytics. 0 of those listings publish a pay range, a disclosure rate of 0%.

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What they're asking for

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

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job 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 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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