Data QE- Specialist

Company Description Tremend is the newest global software engineering hub for Publicis Sapient. For over 20 years, the company has been infusing its advanced technical expertise into complex and innovative solutions that meet today's digital transformation needs and pave the way for a better and smarter future. By joining forces with Publicis Sapient we're accelerating the impact, providing a good mix of talented engineers, technology, continuous improvement, innovation, and R&D. Here, you'll have the opportunity to unleash your potential, powering up advanced software solutions for some of the world's most iconic brands. Embrace your passion for technology, creativity, and continuous improvement, and join us in making a difference through engineering. Job Description 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 Additional Information Besides an exciting job in a tremendous team, here's what you can expect: A fast-paced tech environment Continuous growth & learning Open feedback culture Room for own initiative & ideas Transparency about results & strategy Recognition & reward for hard work Working with a flexible schedule Medical subscription Meal tickets Extra vacation days - starting with 25 vacation days Many others perks