Senior Full Stack Developer

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.

Collaboration

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

Mandatory Skills:

• 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:The candidate should demonstrate hands-on experience in source-to-target reconciliation, data profiling, completeness/uniqueness/consistency checks, schema drift detection, data lineage validation, KPI verification, automated quality checks using PySpark/SQL, batch/streaming validation, exception handling, root-cause analysis, and release-quality gates.

Preferred Skills

• Event Hubs / Kafka
• Delta Lake
• Microsoft Fabric
• Purview
• Airflow
• Power BI
• DataOps and DevOps practices
• Experience with large-scale enterprise data platforms