Senior Full Stack Developer
Bengaluru · Senior
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Job description
Job Summary
- Vestas is seeking an experienced Data Engineer to design, develop, and maintain scalable Azure-based data pipelines while enforcing robust data quality controls, validation frameworks, and operational monitoring. The role will be responsible for implementing quality gates across the data lifecycle to ensure accuracy, completeness, consistency, reliability, and governance of enterprise data products.
- The engineer will work closely with business stakeholders, data architects, analytics teams, and platform engineers within the Vestas Data & AI ecosystem.
Key Responsibilities
- Design, develop, and maintain enterprise-scale data pipelines using Azure services.
- Build and optimize ETL/ELT workflows using Azure Data Factory (ADF), Azure Databricks, and Azure Data Lake Storage (ADLS).
- Implement automated data quality checks, validation frameworks, and quality gates across ingestion, transformation, and consumption layers.
- Develop reconciliation processes between source and target systems.
- Perform data profiling, data completeness checks, schema validation, and anomaly detection.
- Create monitoring dashboards, alerts, and operational controls for pipeline health and data quality.
- Establish validation controls for Bronze, Silver, and Gold data layers.
- Collaborate with business teams to define critical data quality KPIs and acceptance criteria.
- Support root-cause analysis, defect resolution, and continuous improvement initiatives.
- Ensure adherence to security, governance, and data management standards.
- Participate in code reviews, CI/CD processes, and release governance activities.
Skill Requirements
- Azure Data Factory (ADF)
- Azure Databricks
- Azure Data Lake Storage Gen2 (ADLS)
- PySpark and Spark SQL
- Advanced SQL
- ETL/ELT Pipeline Development
- Data Warehousing Concepts
- Data Modeling (Fact, Dimension, Star Schema, SCD)
- Azure DevOps and CI/CD
- Data Quality Frameworks and Validation Techniques
- Pipeline Monitoring and Observability
Other Requirements
Data Quality & Quality Gate Expertise
The candidate should demonstrate hands-on experience in:
- Source-to-target reconciliation
- Data profiling and data validation
- Completeness, uniqueness, consistency, and accuracy checks
- Schema drift detection
- Data lineage and traceability validation
- KPI verification and business rule validation
- Automated quality checks using PySpark and SQL
- Batch and streaming data validation
- Exception handling and audit logging
- Root-cause analysis and quality issue remediation
- Data certification and release-quality gates
These capabilities are aligned with Azure ecosystem quality engineering practices referenced in internal Azure Data Quality requirements. [Role: Data...sure end-t | Viva Engage]
Preferred Skills
- Event Hubs / Kafka
- Delta Lake
- Microsoft Fabric
- Purview
- Airflow
- Power BI
- DataOps and DevOps practices
- Experience with large-scale enterprise data platforms
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