Develop and implement data quality strategies to maintain accuracy and reliability across data systems and products

Lead efforts to enhance data quality by embedding best practices into team workflows and processes

Design and execute advanced testing methodologies and frameworks to ensure enterprise-level data quality standards are met

Manage complex data quality tasks efficiently, prioritizing under tight deadlines and competing requirements

Create tailored testing strategies aligned with evolving system architectures and data pipeline needs

Provide guidance on resource allocation and prioritize testing efforts to meet compliance and business objectives

Establish and continuously improve governance frameworks to ensure adherence to industry standards

Develop and scale automated validation pipelines to support production environments

Work collaboratively with cross-functional teams to troubleshoot infrastructure challenges and optimize system performance

Mentor junior team members and maintain detailed documentation of testing methodologies and strategies

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

Advanced skills in Python for data validation and automation workflows

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

Practical experience with NoSQL databases such as Cassandra, MongoDB, or HBase for managing large datasets

Proficiency in data visualization tools like Tableau, Power BI, or Tibco Spotfire for analytics and decision-making support

Extensive experience with cloud services such as AWS, Azure, or GCP, with an understanding 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 such as 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

Comprehensive understanding of testing frameworks such as TDD, DDT, and BDT for data-focused systems

Experience with CI/CD pipeline implementation using tools like Jenkins or GitHub Actions

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

Excellent verbal and written English communication skills (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 understanding of XPath for data validation and transformation processes

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