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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.
Responsibilities
- 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
Requirements
- 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
Nice to have
- 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