Sr Data Quality Engineer - Hybrid role in Chicago IL

Data Quality Engineering & FrameworksDesign and implement enterprise-wide data quality frameworks aligned to Lakehouse 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 & ComplianceEnsure 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 & DevOpsImplement CI/CD practices for data quality rules, validations, and monitoring