Group Lead - Data Quality Engineer
DP World
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Job Description
KEY ACCOUNTABILITIES
- Data Quality Policy & Framework Implementation
- Define and operationalize enterprise Data Quality policies, procedures, and standards.
- Establish standardized data quality dimensions and certification frameworks.
- Implement scalable validation frameworks across ingestion, transformation, and serving layers.
- Embed quality-by-design principles into data product lifecycle.
- Data Observability Platform Development
- Design and implement end-to-end data observability capabilities including:
- Data freshness and SLA monitoring
- Volume and distribution anomaly detection
- Schema drift and pipeline health monitoring
- Data lineage validation and reliability tracking
- Develop automated alerting and incident detection mechanisms.
- Custom Data Applications (DataApps) Development
- Build custom Data Quality and Observability applications using:
- Databricks native capabilities
- Streamlit / Databricks Apps
- Python-based backend services
- Develop user interfaces enabling:
- Data quality rule configuration
- Dataset certification workflows
- Quality score visualization
- Issue tracking and remediation workflows
- Enable self-service quality monitoring for engineering and analytics teams.
- Azure & Databricks Platform Integration
- Implement data quality checks within Azure-based data pipelines and Databricks workflows.
- Integrate monitoring with:
- ADLS Gen2
- Databricks Lakehouse architecture
- Batch and streaming pipelines
- Develop reusable frameworks leveraging Spark and Delta Lake.
- Optimize performance and scalability of quality validation workloads.
- Automation & Engineering Excellence
- Integrate DQ checks into CI/CD and deployment pipelines.
- Develop metadata-driven quality monitoring solutions.
- Implement automated remediation and self-healing workflows where applicable.
- Ensure auditability, traceability, and governance compliance.
- Metrics, Reporting & Adoption
- Define enterprise Data Quality KPIs and reliability SLAs.
- Build dashboards tracking platform-wide data trust scores.
- Drive adoption of standardized DQ practices across engineering teams.
- Support audit and compliance reporting initiatives.
- Data Quality Score
- Leadership & Collaboration
- Act as technical lead for Data Quality and Observability engineering.
- Mentor engineers on best practices for data reliability.
- Collaborate with Data Engineering, Governance, and Platform Architecture teams.
- Contribute to long-term evolution of the enterprise data platform.
Education
- Bachelor's or master's degree in computer science, Data Engineering, Information Systems, or related field.
- 8+ years of experience in Data Quality engineering roles within Data Platforms/Data Engineering teams.
- Proven experience building custom applications on Databricks or data platforms.
- Experience designing enterprise Data Quality or Data Observability solutions.
- Hands-on experience developing internal data tools or platform applications.
- Cloud & Data Platform
- Microsoft Azure
- Databricks Lakehouse platform
- ADLS Gen2
- Distributed data processing using Spark
- Application & DataApp Development
- Experience building DataApps using:
- Streamlit
- Databricks Apps or notebook-based applications
- Python backend development
- Experience designing UI-driven data engineering tools or internal platforms.
- Data Quality & Observability
- Experience implementing data validation frameworks.
- Strong SQL and Python programming skills.
- Knowledge of anomaly detection, monitoring, and data reliability concepts.
- Engineering & Integration
- CI/CD integration for data pipelines.
- REST API integrations and automation workflows.
- Metadata-driven architectures and lineage concepts.
- Platform-first engineering mindset.
- Strong problem-solving and analytical thinking.
- Ability to translate governance requirements into scalable technical solutions.
- Strong stakeholder collaboration and communication skills.
- Ownership mindset with ability to lead initiatives end-to-end.
- Experience with Great Expectations, Deequ, Soda, or similar frameworks.
- Experience with streaming data validation.
- Exposure to AI-driven data observability or anomaly detection.
- Experience building enterprise internal developer platforms.
About The Company
DP World
Trade is the lifeblood of the global economy, creating opportunities and improving the quality of life for people around the world. DP World exists to make the world’s trade flow better, changing what’s possible for the customers and communities we serve globally. With a dedicated, diverse and professional team of more than 108,000 employees, spanning 74 countries on six continents, DP World is pushing trade further and faster towards a seamless supply chain that’s fit for the future. We’re rapidly transforming and integrating our businesses -- Ports and Terminals, Marine Services, Logistics and Technology – and uniting our global infrastructure with local expertise to create stronger, more efficient end-to-end supply chain solutions that can change the way the world trades. What's more, we're reshaping the future by investing in innovation. From intelligent delivery systems to automated warehouse stacking, we’re at the cutting edge of disruptive technology, pushing the sector towards better ways to trade, minimising disruptions from the factory floor to the customer’s door. We make trade flow, to change what’s possible for everyone.
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