[Hiring] Senior Data Quality Engineer @EPAM

Role Description

We are seeking a Senior Data Quality Engineer to design and implement robust data quality strategies, drive governance processes, and build automation frameworks that ensure data products meet the highest standards across the organization.

• Collaborate with product, engineering and customer teams to understand requirements and design a data quality strategy
• Play a key role in data governance processes, including data preparation, generation, obfuscation, integration, slicing and quality control
• Perform testing of data pipelines, ETLs, APIs, integration, and performance checks
• Prepare test data sets and perform data profiling and benchmarking
• Design and implement a quality verification strategy for data products and ensure all business areas meet defined standards
• Prepare Data Quality environments and applications in compliance with standards and contribute to CI/CD process establishment
• Participate in designing and maintaining data platforms, build a data quality automation framework, and troubleshoot related issues

Qualifications

• 3+ years of hands-on engineering experience in Data Management, Data Quality verification/Data Governance, and Data Integration
• Understanding of data pipelines, Data Lakes, and ETL testing
• Knowledge of CI/CD principles and best practices in data processing
• Proficiency in SQL (aggregation, window functions)
• Experience in Python or other scripting languages
• Experience with one of the major cloud providers: AWS, Azure or GCP
• Experience in building a test automation framework
• Understanding of Big Data principles
• Experience in data analysis and requirements validation
• Experience in maintaining QA environments
• Experience in Data project Test Planning, Test Case design and test result reporting
• Analytical approach to problem solving with excellent interpersonal and communication skills
• Experience in direct customer communications
• English proficiency at B2 level or higher

Requirements

• Familiarity with data processing technologies such as Spark, Hadoop, Kafka, Elasticsearch, and Python libraries (Pandas/NumPy)
• Understanding of various ETL Tools (e.g., Databricks)
• Knowledge of Linux and Bash scripting basics
• Skills in infrastructure troubleshooting, performance tuning and optimization, and bottleneck problem analysis
• Experience in Data project Test Strategy creation