Senior Data Quality Engineer (4 Months Contract ) Onsite in UAE - Octopus by RTG

About the Role

We are seeking an experienced Senior Databricks Data Quality Engineer to lead the design, implementation, and automation of enterprise-scale data quality frameworks within a Databricks environment. The successful candidate will play a key role in establishing data quality controls, profiling frameworks, remediation processes, and AI-assisted quality monitoring across a large-scale data platform consisting of 170+ datasets and over 1,300 Critical Data Elements (CDEs).

This role requires strong expertise in Databricks, PySpark, Delta Lake, MLflow, and modern data quality management practices.

Key Responsibilities

Data Platform & Databricks Configuration

• Configure and manage Databricks workspaces, compute clusters, PySpark notebooks, Delta Lake architecture, and Unity Catalog integrations.
• Design scalable data quality processing frameworks across 170+ datasets and 1,346 prioritized Critical Data Elements (CDEs).

Data Profiling & Quality Assessment

• Develop AI-assisted profiling notebooks using PySpark to establish baseline data quality scores.
• Assess data quality across six key dimensions including:
• Completeness
• Uniqueness
• Validity
• Consistency
• Accuracy
• Timeliness
• Analyze null rates, duplicate records, invalid values, format violations, outliers, and schema drift.

Data Quality Rule Framework

• Design and build a scalable Data Quality Rule Factory using parameterized PySpark functions.
• Enable automated deployment of over 6,700 data quality rules without manual rule-by-rule development.
• Create reusable rule templates across datasets and data quality dimensions.

Pipeline Quality Enforcement

• Integrate data quality controls within Bronze, Silver, and Gold Delta Lake layers.
• Implement quality gates that prevent data progression unless predefined thresholds are met.
• Develop reusable Databricks Jobs for automated validation and monitoring.

Data Cleansing & AI-Driven Remediation

• Build automated data cleansing pipelines for:
• Standardization
• Deduplication
• Schema harmonization
• Deploy MLflow-managed machine learning models for:
• Anomaly detection
• Fuzzy duplicate detection
• Exact duplicate identification
• Ensure explainability of model outputs and support human-in-the-loop validation processes.

Exception Management

• Design failed-record handling frameworks and quarantine Delta tables.
• Capture failure reasons, affected CDEs, rule references, and timestamps.
• Develop automated reprocessing mechanisms for corrected records.

Data Quality Monitoring & Reporting

• Build Delta Lake aggregation tables for data quality metrics.
• Deliver data quality KPIs to Power BI dashboards including:
• Dimension-level scores
• Rule pass/fail rates
• SLA adherence metrics
• Configure automated alerting using Databricks SQL Alerts and Azure Monitor.

Predictive Data Quality Analytics

• Develop predictive models to identify datasets at risk of quality degradation.
• Support AI-assisted Root Cause Analysis (RCA) using profiling outputs and machine learning techniques.
• Export and prepare remediation datasets for prioritization and governance reporting.

Requirements

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 5+ years of experience in Data Engineering or Data Quality Engineering.
• 3+ years of hands-on experience with Databricks and PySpark.
• Strong expertise in Delta Lake architecture and data pipeline development.
• Experience with Unity Catalog implementation and governance.
• Hands-on experience with MLflow and machine learning deployment.
• Strong SQL skills and data modeling expertise.
• Experience building enterprise-scale data quality frameworks.
• Experience integrating Databricks with Power BI and Azure services.
• Strong understanding of data governance, metadata management, and data quality dimensions.

Preferred Qualifications

• Microsoft Azure certifications.
• Databricks Certified Data Engineer Associate or Professional.
• Experience with enterprise data governance programs.
• Experience implementing AI-assisted data quality and remediation solutions.
• Knowledge of Master Data Management (MDM) principles.