Senior Data Engineer ? Data and Analytics (Remote)

Senior Data Engineer – Data and Analytics (Remote) Fusion Education Group is seeking a curious and tenaciously driven individual to lead data and analytics architecture for a data-aware organization. This opportunity will report to the Manager of Data and Analytics and provide mentorship to a small team of Data Engineers and Visualization Developers. This opening requires excellent planning, coordination, and communication skills. As a Senior Data Engineer at Fusion Education Group you will oversee the design, development, and implementation of data and analytics for the company’s data ecosystem. You will be responsible for the entire development lifecycle, appreciating and translating the needs of the user and ensuring the final product meets their expectations while upholding department and company standards. As a Senior Data Engineer, you will architect, build, and optimize the data infrastructure that powers advanced analytics, machine learning, and business intelligence across the organization. You’ll leverage the latest Azure Data Lake architecture, AI services, and cloud-native tools, collaborating with AI Engineers, Data Engineers, Data Analysts, and business stakeholders to deliver reliable, scalable, and secure data solutions. Responsibilities include but are not limited to: • 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). Required Skills & Qualifications • 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. Preferred Skills • Experience deploying machine learning models to production using Azure ML. • Exposure to real-time data stream