We are looking for a Lead Data Quality Engineer to drive the data quality testing strategy across our products, collaborating with cross-functional teams to ensure data reliability, governance, and performance meet the highest standards.

Responsibilities

Work with product, engineering and customer teams to understand requirements and implement a quality testing strategy, defining and setting up data quality metrics Design and implement a quality verification strategy for data products and ensure that all areas of the business perform according to the defined standards Define priorities, risks and attributes in the data quality testing strategy, as well as choose proper data tools Control and tune the data governance processes, including data preparation, generation, data obfuscation, data integration, data slicing and data quality control Prepare data quality environments and applications in compliance with the standards Design, monitor and maintain QA reports, KPIs and quality trends Collaborate with all teams involved in data solution development to ensure efficient task execution for achieving results Adhere to and promote quality standards Requirements 5+ years of hands-on engineering experience and practice in Data Management, Data Quality verification/Data Governance and Data Integration Understanding of data pipelines, data lakes and ETL testing Experience in CI/CD principles and best practices in data processing Knowledge of SQL Proficiency in Python or other scripting languages Experience with one of the major cloud providers: AWS, Azure or Google Cloud Platform Experience in building up a test automation framework and maintaining QA environments Experience in data analysis and requirements validation Concrete experience in Data project Test Strategy creation, Test Planning, Test Case design and test result reporting Skills in infrastructure troubleshooting, support in performance tuning and optimization, and bottleneck problem analysis Experience in leading a team and in direct customer communications Analytical approach to problem solving, excellent interpersonal and communication skills, and English proficiency Nice to have Familiarity with a few data processing technologies, e.g., Spark, Hadoop, Kafka, Elasticsearch, Python libraries (Pandas/NumPy/etc.) Understanding of various ETL Tools (e.g., Databricks) Knowledge of Linux and Bash scripting basics