Denodo Self-Service Data Platform Engineer
Role Overview
Looking for an experienced Denodo Self-Service Data Platform Engineer to design, develop, and support enterprise data virtualization solutions.
The candidate will enable business users and analysts to access trusted, secure, and governed data through scalable self-service analytics platforms.
Key Skills for This Role
Full Job Posting
Job Summary
Looking for an experienced Denodo Self-Service Data Platform Engineer to design, develop, and support enterprise data virtualization solutions.
The candidate will enable business users and analysts to access trusted, secure, and governed data through scalable self-service analytics platforms.
Key Responsibilities
- Design and develop self-service data products, reusable data services, and virtual views using Denodo.
- Develop and maintain Denodo Virtual DataPort (VDP) views, logical data models, and semantic layers.
- Integrate data from databases, cloud platforms, APIs, ERP systems, and SaaS applications.
- Optimize query performance, caching strategies, and data virtualization architecture.
- Implement data transformation, data quality rules, and data abstraction layers.
- Manage data governance, role-based access control (RBAC), data masking, metadata, data cataloging, and lineage.
- Integrate Denodo with Power BI, Tableau, Qlik, and other BI platforms.
- Monitor platform performance, troubleshoot issues, and support upgrades and releases.
- Prepare technical documentation, data dictionaries, operational procedures, and user training materials.
- Provide Level 2/Level 3 support and collaborate with business and technical teams to improve self-service analytics.
Must-Have Requirements
- Minimum 4+ years in Data Engineering, Data Management, or Analytics Platforms.
- Minimum 2+ years of hands-on experience with the Denodo Data Virtualization Platform.
- Strong experience with Denodo Virtual DataPort (VDP) and Design Studio, including developing and maintaining virtual views.
- Strong understanding of data virtualization architecture, data abstraction, query optimization, caching, and performance tuning.
- Proficiency in SQL, advanced query development, logical and semantic data modeling, and ETL/ELT concepts.
- Experience integrating data from relational databases, cloud platforms, APIs, ERP systems, and SaaS applications.
- Knowledge of RBAC, data masking, metadata management, data cataloging, lineage tracking, and access control.
- Experience integrating or supporting BI tools, particularly Power BI, Tableau, or Qlik.
- Understanding of data transformation, validation, data quality rules, and enterprise data management practices.
- Ability to monitor, diagnose, and resolve data virtualization and query performance issues.
- Ability to prepare technical designs, data dictionaries, operational procedures, and support documentation.
- Strong analytical, problem-solving, communication, and collaboration skills with business and technical stakeholders.
Good-to-Have
- Denodo Certified Professional or Denodo Certified Developer certification.
- Hands-on experience with Denodo Data Catalog, Scheduler, and Solution Manager.
- Experience working with Microsoft Azure, AWS, or Google Cloud Platform (GCP).
- Exposure to Azure Data Factory, Databricks, Azure Synapse Analytics, or Snowflake.
- Experience with Oracle, SQL Server, PostgreSQL, MySQL, Azure SQL, or Amazon Redshift.
- Familiarity with REST/SOAP APIs and enterprise data integration patterns.
- Experience with SAP Analytics Cloud or other enterprise analytics platforms.
- Understanding of Data Mesh and Data Fabric architectures.
- Exposure to enterprise data governance and data catalog solutions.
- Experience with Agile, Scrum, and DevOps methodologies.
- Experience implementing enterprise-wide self-service data and analytics solutions across cloud and on-premises environments.
- Experience delivering user training, knowledge transfer, and technical guidance to business users and analysts.
Key Deliverables
Reusable Denodo virtual views, datasets, and data services.
Optimized and scalable data virtualization solutions.
Secure and governed enterprise data access.
Data catalog, metadata, and lineage documentation.
Improved data accessibility and reporting efficiency.
Increased adoption of self-service analytics capabilities.
Work Arrangement
- Onsite.
Experience and Joining
- Experience: 5-8 years.
- Immediate joiner preferred.
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