Data Quality Engineer

About Gradera Gradera defines a new category of enterprise transformation called Software-Orchestrated Services™ - where software orchestrates human expertise, digital workers, and enterprise systems to deliver governed outcomes at scale. As an AI Native Services firm, we help enterprises redesign how work gets done across operations, product, engineering, customer experience, data, and enterprise workflows to move beyond fragmented AI pilots and disconnected automation toward measurable business outcomes.

Overview We are seeking a detail-oriented

Data Quality Engineer to ensure the integrity, accuracy, and reliability of data powering our digital twin and AI platforms. You will design and implement data quality frameworks, build automated validation pipelines, and establish quality metrics that enable trusted, simulation-ready data products. This role is critical to ensuring that operational decisions and ML models are built on a foundation of high-quality, governed data. Key Responsibilities

• Design and implement data quality frameworks using Delta Live Tables expectations and Great Expectations
• Build automated data validation pipelines that enforce quality standards at ingestion and transformation stages
• Develop data profiling processes to understand data distributions, patterns, and anomalies
• Define and track data quality metrics (completeness, accuracy, consistency, timeliness, validity)
• Implement anomaly detection mechanisms to identify data drift and quality degradation
• Create quality dashboards and alerting systems for proactive issue identification
• Collaborate with data engineers to embed quality checks into ETL/ELT pipelines
• Partner with data architects to establish data quality standards and governance policies
• Investigate and perform root cause analysis for data quality issues
• Document data quality rules, thresholds, and remediation procedures
• Support data certification processes for simulation-ready and ML-ready datasets
• Drive continuous improvement in data quality practices and tooling Our core data quality stack includes:

Data Quality Frameworks

• Delta Live Tables expectations for declarative quality enforcement
• Great Expectations for comprehensive data validation
• Databricks data profiling and quality monitoring

Platform & Tools

• Databricks SQL and PySpark for quality checks at scale
• Unity Catalog for lineage tracking and governance compliance
• Python for custom validation logic and anomaly detection Observability
• Quality metrics dashboards and alerting
• Data profiling and statistical analysis
• Anomaly detection and drift monitoring Preferred Qualifications
• 6+ years of experience in data engineering or data quality roles, with 3+ years focused on data quality
• Track record of implementing enterprise-scale data quality frameworks
• Experience with Lakehouse architectures (Delta Lake, Iceberg)
• Familiarity with real-time data quality monitoring for streaming pipelines
• Experience working in agile, cross-functional teams Highly Desirable
• Experience with data quality for digital twin or simulation platforms
• Familiarity with operational state data validation and temporal consistency checks
• Experience with graph data quality validation (Neo4j or similar)
• Exposure to ML data quality (feature validation, training data quality)
• Experience with data observability platforms
• Exposure to industrial domains such as Manufacturing, Logistics, or Transportation is a plus