Position Summary
We are seeking a highly skilled Senior Data Engineer to design, develop, optimize, and support modern enterprise data platforms and data integration solutions. This role is responsible for building scalable data pipelines, data warehouses, ETL/ELT frameworks, and analytics-ready data architectures utilizing Microsoft Fabric, Azure Data Factory, Fabric Data Factory, SQL Server, and associated technologies. The ideal candidate will possess deep expertise in SQL development, data warehousing methodologies, ETL/ELT best practices, and cloud-based data engineering. Our engineering organization has adopted Specification-Driven Development (SDD) as a core practice emphasizing clear requirements, technical specifications, automation, quality, and maintainability. We also view Artificial Intelligence and AI-assisted engineering as strategic investments in our future.
Key Responsibilities
Design, build, and maintain scalable, reliable, and secure data integration and analytics solutions.
Develop enterprise platforms utilizing Microsoft Fabric, Fabric Data Factory, Azure Data Factory, SQL Server, OneLake, Lakehouse, and Data Warehouse architectures.
Design and implement modern ETL and ELT frameworks supporting analytics, reporting, and operational use cases.
Develop ingestion, transformation, orchestration, monitoring, data quality, and reconciliation processes.
Participate in Specification-Driven Development practices and create technical specifications and design documentation.
Design solutions supporting AI, machine learning, generative AI, and advanced analytics initiatives.
Support CI/CD, monitoring, production operations, root-cause analysis, and continuous improvement.
Required Qualifications
Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or related field, or equivalent practical experience.
7+ years of experience in data engineering, data warehousing, ETL/ELT development, or related disciplines.
Experience designing and implementing enterprise-scale data warehouse and analytics platforms.
Experience with Azure cloud-based data engineering solutions and Agile delivery practices.
Required Technical Skills
Microsoft Fabric (Lakehouse, Data Warehouse, Data Factory, OneLake)
Azure Data Factory and Fabric Data Factory
SQL Server and advanced T-SQL
ETL and ELT architecture and development
Dimensional modeling and data warehouse design
Data orchestration and automation
Data governance and data quality
Git and Azure DevOps
Preferred Qualifications
Pentaho Data Integration (PDI/Kettle)
SQL Server Integration Services (SSIS)
Azure Synapse Analytics
Azure Data Lake Storage
Spark and PySpark
Power BI
Data Vault methodology
Infrastructure as Code
CI/CD for data platforms
Experience with SDD methodologies
Experience supporting AI, machine learning, generative AI, or advanced analytics
Legacy Platform Experience (Beneficial)
Qlik Replicate
Qlik Compose
Legacy ETL migration and modernization programs
CDC and data replication architectures
Migration of legacy data solutions into Microsoft Fabric and Azure environments
Knowledge & Competencies
Kimball dimensional modeling
Fact and dimension design
Slowly Changing Dimensions (Types 1, 2, and 3)
Data quality and governance
CDC and incremental loading strategies
Metadata-driven processing
Performance optimization and scalability
Strong analytical, communication, and problem-solving skills
Success Criteria
Build scalable, secure, and reliable enterprise data pipelines and integration solutions.
Deliver optimized Microsoft Fabric warehouse and lakehouse solutions.
Establish and follow ETL/ELT best practices and engineering standards.
Improve data quality, reliability, and accessibility.
Enable analytics, reporting, AI, and data-driven decision making.
Deliver high-quality solutions through disciplined Specification-Driven Development practices.
Actively contribute to the organization's AI strategy through architecture, automation, and innovation.
Help establish modern engineering practices that improve productivity, quality, and maintainability.
Work on a team leading in the adoption of Microsoft Fabric and AI-enabled data engineering capabilities.
Our Interview Practices
To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.