Create and implement data quality strategies to ensure consistent accuracy across data systems and products

Lead initiatives to improve data workflows by incorporating best practices across teams and projects

Develop and apply advanced testing frameworks and methodologies to meet enterprise data quality standards

Efficiently manage complex data quality tasks, ensuring prioritization and delivery under tight deadlines

Design testing strategies tailored to evolving system architectures and data pipeline requirements

Provide recommendations on resource allocation and testing priorities that align with compliance and business needs

Establish and refine governance frameworks to ensure alignment with industry standards

Build and optimize automated validation pipelines to support production environments

Collaborate with cross-functional teams to resolve infrastructure challenges and improve system performance

Mentor junior engineers and maintain comprehensive documentation of testing processes and strategies

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

Advanced proficiency in Python for data validation and workflow automation

Expertise in Big Data platforms such as Hadoop tools (HDFS, Hive, Spark) and modern streaming technologies like 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 for analytics and reporting

Extensive experience with cloud platforms like AWS, Azure, or GCP, with knowledge of multi-cloud architectures

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

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

Familiarity with MDM tools and performance testing applications like JMeter

Strong experience with version control systems like Git, GitLab, or SVN, and automation for large-scale systems

Deep understanding of testing frameworks such as TDD, DDT, and BDT for data-focused environments

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

Strong analytical and problem-solving abilities, with the capability to derive actionable insights from complex datasets

Excellent verbal and written communication skills in English (B2 level or higher), with experience engaging stakeholders

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

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