Job Description
Key Skills:
Testing in DataBricks, Pytest Framework, Automation Testing, API Testing, Data Testing, SQL. Job Description:
Data Quality Engineering & Frameworks Design and implement enterprise-wide data quality frameworks aligned to Lake house architecture (bronze, silver, gold layers) Define and enforce data quality rules including completeness, accuracy, consistency, timeliness, and validity Develop reusable data validation, reconciliation, and monitoring patterns within Databricks pipelines Establish automated data quality checks embedded within ELT/ETL workflows Databricks & Pipeline Integration Integrate data quality controls directly into Databricks (Spark/Delta Lake) pipelines and workflows Develop scalable validation processes for batch and event-driven ingestion pipelines Partner with Data Engineers to ensure quality gates are enforced across ingestion, transformation, and consumption layers Optimize data quality processes for performance and scalability within large distributed datasets Monitoring, Observability & Issue Management Implement and manage data observability frameworks, including metrics, alerts, and dashboards Monitor data pipelines and proactively identify anomalies, failures, and quality degradation Lead root cause analysis (RCA) efforts for data quality issues and drive remediation Develop and maintain quality scorecards and reporting for stakeholders Data Governance & Compliance Ensure adherence to enterprise data governance standards, including metadata, lineage, and auditability Partner with Data Governance teams (e.g., Collibra) to align data definitions, ownership, and controls Support regulatory requirements (e.g., SOX, GLBA, data integrity standards) through auditable data quality controls Define and enforce data quality SLAs and data contracts across domains Automation & DevOps Implement CI/CD practices for data quality rules, validations, and monitoring Automate testing frameworks for validating data transformations and pipelines Develop reusable libraries and frameworks for enterprise-scale data quality enforcement Collaboration & Leadership Partner with Data Engineers, Data Architects, BI teams, and business stakeholders to embed quality-by-design principles Provide technical leadership and mentorship on data quality best practices Act as a subject matter expert (SME) for data quality across the organization Drive continuous improvement and innovation in data quality tooling and methodologies Required Qualifications:
5+ years of experience in data engineering, data quality engineering, or related roles Strong hands-on experience with Databricks, Spark (PySpark), and Delta Lake Proven experience implementing data quality frameworks and controls in modern data platforms Advanced SQL and data profiling/validation skills Experience working with large-scale datasets in cloud environments (AWS or Azure) Experience integrating data quality into ELT/ETL pipelines and orchestration tools Strong understanding of data governance and data lifecycle management Preferred Qualifications :
Experience in financial services or regulated environments Familiarity with data governance tools (e.g., Collibra) Experience with data observability or quality tooling (e.g., Monte Carlo, Great Expectations, Deequ, or similar) Experience with real-time data quality validation (streaming pipelines) Knowledge of regulatory reporting and data controls frameworks Cloud or Databricks certifications Technical SkillsDatabricks (Lakehouse, Unity Catalog, workflows) Spark / PySpark SQL (advanced) Delta Lake Data quality frameworks (rule engines, validation patterns) Data observability and monitoring Cloud platforms (AWS or Azure) Orchestration tools (Airflow, Control-M) APIs and data integration CI/CD and DevOps Data modeling and lineage concepts Professional Competencies:
Strong analytical and problem-solving skills with a focus on data integrity High attention to detail and commitment to data accuracy Strong communication skills across technical and non-technical stakeholders Ability to influence standards and drive enterprise adoption Collaborative mindset with a focus on continuous improvement
