Quality Assurance Automation Engineer

About Brillio Brillio LLC is a fast-growing, pure-play Digital Transformation Solutions and Services company backed by Bain Capital private equity. Founded in 2014 and headquartered in Silicon Valley, Brillio is focused on delivering design-led solutions for our customers. We are not an IT Services company trying to pivot and support the “next big thing.” As a digitally native company, everything we have done and will continue to do, will have this laser focus. Brillio delivers disruptive digital solutions across capabilities such as Design Thinking, Product Engineering, Data Analytics, Digital Front Office, and Digital Infrastructure. Our core values of Customer success, We Care, Entrepreneurial (mind set) and Excellence - drive everything that we do, from the very first day. Brillio Romania Brillio Romania is a dynamic and rapidly growing company with over 200 employees and offices in Cluj, Oradea, and Bucharest. With its fast-paced growth, the company has consistently demonstrated an unwavering commitment to client satisfaction. At Brillio Romania, we know our success comes from the innovative contributions and brilliant work of our people. So, we make fostering a positive work environment our top priority. Employees at Brillio Romania not only thrive, but they also have the chance to build long and fulfilling careers, fostering a sense of stability and dedication that ultimately benefits both the company and our valued clients. Role: Senior QA Engineer Role Summary We are looking for a Data Quality Assurance Engineer to ensure the accuracy, consistency and reliability of our data assets across analytics and machine learning platforms. The successful candidate will design and implement data quality frameworks, automate validation and testing, and collaborate with engineering and data science teams to maintain high standards of data integrity throughout the data lifecycle. Responsibilities Develop and maintain data quality frameworks, policies and test plans to validate data ingests, transformations and outputs across pipelines. Design and implement automated data tests using PySpark, SQL and orchestration tooling to detect anomalies, schema drift and data regressions. Build and operate monitoring, alerting and observability for data quality metrics and SLAs, integrating with existing logging and incident management systems. Define and implement data validation rules, checksums, reconciliation jobs and sampling strategies for batch and streaming data flows. Collaborate closely with Data Engineers, Data Scientists and Product Owners to translate data quality requirements into actionable tests and remediation plans. Author and maintain documentation, runbooks and playbooks for data quality processes, triage steps and escalation paths. Perform root cause analysis on data incidents, drive corrective actions and work with engineering teams to prevent recurrence. Implement data lineage, provenance and metadata capture to support traceability and audit requirements. Support data governance and compliance efforts by validating access controls, masking and retention policies as they relate to data quality. Contribute to CI/CD pipelines for data quality artefacts and ensure tests run as part of deployment workflows. Mentor colleagues on best practices for testing, validation and monitoring of data products. Requirements 3+ years of experience in data quality, data engineering, QA or a closely related role working with production data platforms. Strong practical experience with SQL and Python; familiarity with PySpark or Spark‑based development is desirable. Experience designing and implementing automated data tests, validation frameworks and reconciliation processes. Knowledge of data modelling concepts, lakehouse/warehouse patterns and common data formats (Parquet, Delta, Avro, JSON). Experience with monitoring and observability tools, alerting systems and metrics-driven incident management. Familiarity with data pipeline orchestration (e.g. Airflow, Fabric Pipelines, Data Factory) and CI/CD practices for data workloads. Understanding of data governance, lineage and metadata management tools and principles. Analytical mindset with strong troubleshooting skills, attention to detail and a pragmatic approach to resolving data issues. Excellent communication and collaboration skills; able to work with cross‑functional stakeholders to define acceptance criteria and remediation plans. Self‑directed, curious and committed to continuous improvement of data quality practices and tooling.