Data Quality Engineer, Data Reliability Platform

Remote.Bulgaria
Remote.Georgia
Remote.Poland
Белград
Варна
Варшава
Вроцлав
Ереван
Клуж-Напока
Краков
Ларнака
Лодз
Люблин
Рига
София
Тбилиси

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Client

Our client is a leading global professional education organization focused on advancing investment knowledge and credentialing standards.

Team

You will join a cross functional team of data engineers, analytics specialists, and quality experts. The team collaborates closely, communicates frequently, and works in an environment where data correctness and reliability are key priorities. Work is structured and iterative, with regular stakeholder interactions.

Position overview

You will work on a platform focused on improving data accuracy, trust, and reliability across analytical and operational environments. The project includes implementing monitoring rules, building automated validation workflows, and supporting data teams with insights that help maintain data quality at scale. The platform is actively evolving and provides space for ownership and continuous improvement.

Technology stack

Monte Carlo
Soda
Great Expectations
SQL
Python
Cloud data platforms
Data pipeline architectures
Metadata and data governance tooling

Responsibilities

Design and implement data quality monitoring rules using Monte Carlo, Soda, Great Expectations or equivalent tools
Perform data profiling to assess completeness, consistency, and accuracy
Collaborate with data engineering and analytics teams to define data quality needs
Develop automated checks and alerts as part of data pipelines
Support remediation workflows and participate in root cause analysis
Document rules, processes, and metrics supporting data quality governance
Contribute to continuous improvement initiatives and propose enhancements
Communicate findings and recommendations to technical and non technical stakeholders

Requirements

Experience working with data quality tools such as Monte Carlo, Soda, or Great Expectations
Hands on background in data profiling, validation, and monitoring
Experience using SQL for analysis and data validation tasks
Familiarity with Python or similar scripting languages for automation
Understanding of data pipeline principles and cloud data platforms
Knowledge of data governance concepts and quality frameworks
Clear communication skills and the ability to collaborate in a distributed team

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