Senior Data Engineer – Data and Analytics (Remote)

Fusion Education Group is seeking a Senior Data Engineer to lead data and analytics architecture for their data-aware organization. In this role, you will oversee the design, development, and implementation of data solutions while mentoring a small team, ensuring that the data infrastructure supports advanced analytics and business intelligence across the organization.

Responsibilities:

• Design, architect and operate modern data platforms on Azure
• Architect and maintain data lakes/lakehouses with Azure Data Lake Storage (ADLS) and Databricks
• Establish robust ELT patterns using Azure Data Factory (ADF) pipelines with a focus on cost, performance, and reliability
• Lead the full lifecycle—from prototyping to production deployment, including pipelines, APIs, front/back-end integration, monitoring, and maintenance
• Design scalable, reliable systems; optimize performance, latency, cost, and integrate telemetry for iterative improvement
• Build intelligent data products using Azure AI
• Productionize ML workflows in Azure Machine Learning (workspaces, model registry, endpoints
• Integrate Azure Cognitive Services (Language, Vision, Speech) and Azure OpenAI Service for enrichment, classification, summarization, and retrieval-augmented generation (RAG)
• Orchestrate LLM applications using Azure ML pipelines, implementing guardrails, prompt versioning, and telemetry
• Deliver low-latency feature stores and micro-batch ingestion for ML and BI workloads
• Data modeling, quality, and observability
• Define canonical and dimensional models; enforce standards (naming, lineage, SCD patterns) across marts and semantic layers (e.g., Power BI/Fabric)
• Security, compliance, and governance (MCP best practices)
• Apply Azure RBAC, Managed Identities, Private Endpoints, Key Vault, and Customer-Managed Keys; enforce policy with Azure Policy and Defender for Cloud
• Performance, reliability, and FinOps
• Tune Databricks compute, caching, partitioning, Z-order, and query plans; optimize storage tiers and autoscaling
• Monitor SLOs/SLIs with Azure Monitor, Log Analytics, and custom metrics; build cost dashboards and alerts for proactive FinOps
• Collaboration, enablement, and leadership
• Partner with business units on projects to define scope and deliverables; enable self-service via curated datasets and semantic models
• Mentor engineers on Microsoft stack best practices; contribute to standards, templates, and reusable components aligned with Microsoft certifications (e.g., Azure Data Engineer, Azure AI Engineer)

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field
• 5+ years of experience in data engineering or AI/ML production systems
• Advanced proficiency in Databricks and at least one programming language (Python, Scala, or Java)
• Knowledge in implementing, fine-tuning, and deploying LLMs
• Strong capabilities in API development, version control, unit testing, and documentation
• Microsoft Certified: Azure Data Engineer Associate (DP-203) and/or Azure AI Engineer Associate (AI-102) strongly preferred; MCP/MCSA/MCSE or equivalent legacy Microsoft certifications a plus
• Hands-on experience with modern data pipeline orchestration tools (Azure Data Factory)
• Deep knowledge of Azure data and AI services (Azure Data Lake, Azure Machine Learning, Azure Cognitive Services, Azure OpenAI)
• Strong understanding of data modeling, warehousing, and data lake architecture
• Experience with DevOps practices and version control (Azure DevOps, Git, CI/CD)
• Excellent communication and collaboration skills
• Microsoft Certified Professional (MCP) or relevant Azure certifications (e.g., Azure Data Engineer Associate, Azure AI Engineer Associate) are highly preferred
• Mentor Data Analytics Team members with best practices in the data space
• Current with industry trends in data engineering, Azure AI, and cloud platforms
• Experience deploying machine learning models to production using Azure ML
• Exposure to real-time data streaming (Azure Event Hubs, Kafka)
• Exposure to Model Context Protocol (MCP) and AI agents