What are the responsibilities and job description for the Cloud BI Engineer (3 Days in OFFICE :: W2 Only) position at Jobs via Dice?
Dice is the leading career destination for tech experts at every stage of their careers. Our client, VAAM Technologies, is seeking the following. Apply via Dice today!
Key Responsibilities
Key Responsibilities
- Design and build end-to-end data pipelines and ingestion workflows using Azure Data Factory and Microsoft Fabric, ensuring scalability, observability, and data quality at every stage.
- Develop and maintain serverless automation and event-driven integrations using Azure Function Apps and Logic Apps, including orchestration flows that connect AI services to data pipelines.
- Own infrastructure-as-code practices using Terraform to provision and manage cloud resources consistently across environments.
- Drive CI/CD pipeline development and deployment automation through GitHub Actions and Harness, ensuring fast, safe, and repeatable deployments across both data and AI workloads.
- Integrate Azure AI Services and Azure OpenAI into data workflows - including document intelligence, embeddings, semantic search, and AI-assisted data enrichment - enabling the platform to support intelligent applications and copilot-style experiences.
- Architect/Manage analytical workloads within Azure Synapse Analytics, including Lakehouse patterns, dedicated SQL pools, and Spark-based transformations.
- Support cross-cloud strategies by applying AWS knowledge to maintain contingency pipelines and hybrid architecture patterns.
- Maintain continuity of existing pipelines and maintenance work.
- Around 10 years of experience in data engineering with a strong focus on cloud-native solutions.
- Deep hands-on expertise with the Microsoft Azure data stack, including Azure Data Factory, Microsoft Fabric, Synapse Analytics, Logic Apps, and Function Apps.
- Proven experience with infrastructure-as-code using Terraform, and CI/CD platforms such as GitHub Actions and Harness.
- Strong proficiency in SQL and at least one data-oriented programming language, with Python preferred given its centrality to AI and ML integration work.
- Demonstrated experience integrating Azure AI Services or Azure OpenAI into data pipelines or platform workflows, including working with REST-based AI APIs, prompt engineering, embeddings, or AI-assisted data processing.
- Practical experience publishing data to SharePoint Lists from ADF pipelines, including familiarity with SharePoint Online REST APIs and OAuth authentication flows.