Data Quality Engineer

About Gradera — Digital Twin & Physical AI Platform 

At Gradera, we are building a next-generation Digital Twin and Physical AI platform that enables enterprises to model, simulate, and optimize complex real-world systems. Our work brings together strategy, architecture, data, simulation, and experience design to power decision-making across large-scale operational environments such as manufacturing, logistics, and supply chain networks. 

This platform-led initiative applies AI-native execution, advanced simulation, and governed orchestration to help organizations test scenarios, predict outcomes, and continuously improve performance. We operate with an enterprise-first mindset prioritizing reliability, transparency, and measurable business impact as we build intelligent systems that scale beyond a single industry or use case. 

Data Quality Engineer 

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. 

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 

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 

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 

Location: Hyderabad, Telangana 
Department: Engineering 
Employment Type: Full-Time