Oversee end-to-end data quality strategy, ensuring rigorous testing and reliability of data products and processes

Drive data quality initiatives while instilling best practices across multiple teams and projects

Define and enforce advanced testing methodologies and frameworks to ensure enterprise-level data quality

Prioritize and manage complex data quality tasks, optimizing efficiency under tight timelines and competing demands

Architect and maintain robust testing strategies tailored to evolving system architectures and data pipelines

Advise on resource allocation, setting priorities for testing aligned with regulatory and business standards

Establish and continuously enhance a robust data quality governance framework, overseeing compliance with industry standards

Develop, scale, and refine automated data quality validation pipelines for production systems

Collaborate at a high level with cross-functional teams to resolve infrastructure challenges and optimize performance

Mentor junior engineers while maintaining comprehensive documentation, including versions of test strategies and advanced test plans

3+ years of professional experience in Data Quality Engineering

Advanced programming skills in Python

Deep expertise in Big Data platforms, including Hadoop ecosystem tools (HDFS, Hive, Spark) and modern streaming platforms (Kafka, Flume, Kinesis)

Strong practical knowledge of NoSQL databases such as Cassandra, MongoDB, or HBase, with a track record of handling enterprise-scale datasets

Advanced skills in data visualization and analytics tools (e.g., Tableau, Power BI, Tibco Spotfire) to support decision-making

Extensive hands-on experience with cloud ecosystems like AWS, Azure, and GCP, including a strong understanding of complex multi-cloud architectures

Demonstrated expertise with relational databases and SQL (PostgreSQL, MSSQL, MySQL, Oracle) in high-volume, real-time environments

Proven ability to implement, troubleshoot, and scale ETL processes using tools like Talend, Informatica, or similar platforms

Experience deploying and integrating MDM tools into existing workflows, with knowledge of performance testing tools such as JMeter

Advanced experience in version control systems (Git, GitLab, or SVN) and automation/scripting for large-scale systems

Comprehensive knowledge of modern testing frameworks (TDD, DDT, BDT) and their application in data-focused environments

Familiarity with CI/CD practices, including implementation of pipelines using tools like Jenkins or GitHub Actions

Highly developed problem-solving abilities and an analytical mindset capable of interpreting complex datasets into actionable business outcomes

Exceptional verbal and written communication skills in English (B2 level or higher), paired with experience guiding discussions with stakeholders

Extensive hands-on experience with programming languages like Java, Scala, or advanced Bash scripting for production-level data solutions

Advanced knowledge of XPath and its applications in data validation or transformation workflows

Experience designing customized data generation tools and sophisticated synthetic data techniques for testing scenarios