Data QE- Specialist

Tremend is looking for a Data QE who combines strong technical skills with a leadership mindset. You’ll be hands-on in delivering quality across Web, API, and backend layers, while also helping to shape QE practices, mentor peers, and foster a culture of ownership and continuous improvement.

Responsibilities:

• Design and implement robust, scalable testing and quality engineering strategies for data pipelines, data lakes, and data warehouses across the organization
• Build and evolve metadata-driven, automated data quality frameworks for batch, CDC, and streaming data pipelines across modern cloud data platforms
• Design and execute automated validation frameworks using Python, PySpark, and SQL to ensure data integrity, completeness, accuracy, and consistency throughout the data lifecycle
• Define and execute end-to-end reconciliation strategies for ETL/ELT and CDC flows to ensure parity between source systems and downstream analytical targets
• Create comprehensive test plans, test cases, and automated regression suites focused on data quality, schema validation, transformation accuracy, and business-rule compliance in platforms such as Databricks and Snowflake
• Implement schema evolution controls, data contract testing, and automated drift detection to prevent downstream data breakages
• Build and maintain observability for data quality, including metrics, SLAs, alerts, dashboards, and runbooks for data health, reliability, and lineage
• Validate data transformations, performance, and storage strategies, including partitioning, clustering, and cost-aware optimization approaches
• Implement large-scale reconciliation techniques such as hashing, checksums, sampling, and incremental validation for high-volume datasets
• Integrate data security and compliance checks into the quality engineering process, including PII detection, masking validation, and test data controls
• Collaborate with data engineers, analysts, product teams, and business stakeholders to translate requirements and data risks into effective quality assurance strategies
• Identify, document, track, and help resolve data quality issues, including supporting production incident investigation and root cause analysis
• Provide technical consultation and leadership on data quality best practices, testing methodologies, and quality engineering standards
• Create clear documentation for testing procedures, automation frameworks, reconciliation approaches, and data quality metrics
• Participate in sprint planning, backlog refinement, and quality governance activities to ensure quality is built into the development process
• Mentor junior team members and help uplift Data QE capabilities across engineering and analytics teams
• Support proposals, proof of concepts, and client or executive discussions as a Data QE subject matter expert when needed

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience
• 8+ years of experience in quality engineering, data engineering, data QA, with strong hands-on ownership of data quality in warehouse, lake, or lakehouse environments
• Strong proficiency in Python, PySpark, and SQL for data validation, test automation, reconciliation, and ETL/ELT testing
• Strong hands-on experience with Databricks and/or Snowflake, including testing data workflows and validating complex transformations
• Experience working with data lakes, data warehouses, lakehouse architectures, and modern data platform patterns
• Strong knowledge of data quality engineering methodologies, including profiling, reconciliation, schema validation, lineage awareness, metadata-driven validation, and drift detection
• Experience validating batch, CDC, and streaming data pipelines using tools and platforms such as Airflow, Kafka, Kinesis, or equivalent technologies
• Knowledge of CI/CD pipelines and automated test integration for continuous testing of data solutions
• Advanced understanding of reconciliation techniques such as hashing, checksums, sampling, statistical validation, and large-scale data comparison approaches
• Experience with observability and monitoring for data quality, including metrics, dashboards, alerting, and SLA-based controls
• Good understanding of data security, privacy, masking, and subsetting techniques for testing and regulated environments
• Experience with version control systems such as Git for managing test code, automation assets, and quality frameworks
• Familiarity with data modeling concepts, including dimensional modeling and Data Vault, is a plus
• Experience working in Agile delivery environments
• Strong communication skills and the ability to collaborate with both technical and non-technical stakeholders
• Ability to mentor team members and promote best practices in data quality engineering
• Proactive approach to identifying, preventing, and resolving data quality risks