Gradera
Technology
DataQualityEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Quality Engineer at Gradera. Skills: Data quality, Data engineering, ML/AI platforms. Design data quality frameworks. Implement data quality frameworks”
What You'll Achieve.
Simulation-ready data products; ML-ready datasets
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
6+ years experience, 3+ years data quality, Data quality frameworks, Automated validation pipelines, Quality metrics, SQL proficiency, Python proficiency
Nice to Have
6+ years experience, 3+ years data quality, Enterprise-scale data quality frameworks, Lakehouse architectures, Real-time data quality monitoring, Agile, cross-functional teams, Data quality for digital twin, Simulation platforms, Operational state data validation, Temporal consistency checks, Graph data quality validation, ML data quality, Feature validation, Training data quality, Data observability platforms, Industrial domains experience
What You'll Do.
Design data quality frameworks
Implement data quality frameworks
Build automated validation pipelines
Enforce quality standards
Develop data profiling processes
Understand data distributions
Understand data patterns
Understand data anomalies
Define data quality metrics
Track data quality metrics
Implement anomaly detection mechanisms
Identify quality degradation
Create quality dashboards
Create alerting systems
Collaborate with data engineers
Partner with data architects
Establish data quality standards
Establish governance policies
Investigate data quality issues
Perform root cause analysis
Document data quality rules
Document remediation procedures
Support data certification processes
Drive continuous improvement
How You'll Work.
Team & Collaboration
Data engineers; Data architects; Cross-functional teams
Full Job Description
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 con
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