Key skills
Azure Data LakeDatabricksData pipeline orchestrationMachine LearningPythonAPI developmentData modelingAzure DevOpsVersion controlCollaboration skillsJavaScalaAIMLLLMOpenAIRAGELTData EngineeringData LakeAnalyticsBIPower BIKafkaUnit TestingAzureVaultAzure MonitorGitVersion ControlCachingCI/CDLeadershipPrototypingCommunicationCollaboration
About this role
Fusion Education Group is seeking a curious and tenaciously driven individual to lead data and analytics architecture for a data aware organization. As a Senior Data Engineer, you will oversee the design, development, and implementation of data and analytics for the company’s data ecosystem while providing mentorship to a small team of Data Engineers and Visualization Developers.
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
- Florida residents require level 2 background screening through https://info.flclearinghouse.com/
- 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