Develop and oversee data quality strategies to ensure consistent accuracy across data products and processes

Lead initiatives to improve data quality, embedding best practices across teams and projects

Create and deploy advanced testing frameworks and methodologies to uphold enterprise-level data quality standards

Manage complex data quality tasks, ensuring efficiency and prioritization within tight deadlines

Design robust testing strategies tailored to evolving system architectures and data pipelines

Provide strategic direction on resource allocation, aligning testing priorities with business and compliance requirements

Establish and refine governance frameworks to ensure adherence to industry data standards

Build and scale automated validation pipelines to support production systems

Collaborate with cross-functional teams to resolve infrastructure issues and optimize system performance

Mentor junior engineers and maintain comprehensive documentation for testing strategies and plans

Minimum of 3 years of professional experience in Data Quality Engineering or related roles

Advanced proficiency in Python for automation and data validation tasks

Expertise in Big Data platforms, including Hadoop tools like HDFS, Hive, and Spark, as well as modern streaming technologies such as Kafka, Flume, or Kinesis

Hands-on experience with NoSQL databases like Cassandra, MongoDB, or HBase for managing large-scale datasets

Proficiency in data visualization tools such as Tableau, Power BI, or Tibco Spotfire to support analytics and decision-making

Extensive experience with cloud platforms like AWS, Azure, or GCP, with a solid understanding of multi-cloud architectures

Advanced knowledge of relational databases and SQL (PostgreSQL, MSSQL, MySQL, Oracle) in high-volume environments

Proven expertise in implementing and scaling ETL processes using tools like Talend, Informatica, or equivalent platforms

Familiarity with MDM tools and performance testing solutions like JMeter

Advanced experience with version control systems such as Git, GitLab, or SVN, and automation for large-scale systems

Comprehensive knowledge of testing frameworks like TDD, DDT, and BDT for data-centric environments

Experience implementing CI/CD pipelines using tools such as Jenkins or GitHub Actions

Strong analytical and problem-solving skills, with the ability to translate complex datasets into actionable insights

Excellent English communication skills (B2 level or higher), with experience engaging stakeholders and leading discussions

Experience with additional programming languages like Java, Scala, or advanced Bash scripting for production-level solutions

Advanced knowledge of XPath for data validation and transformation workflows

Expertise in designing custom data generation tools and synthetic data techniques for testing scenarios