Key Responsibilities 

• Solution Ownership & Delivery: Own end-to-end delivery from requirements to architecture, implementation, deployment, documentation, and support. 

• Enterprise Data Architecture: Design secure, scalable data lake, lakehouse, warehouse, and data product architectures with reusable patterns. 

• Microsoft Fabric Platform Engineering: Implement Fabric Lakehouse, Warehouse, OneLake, Data Factory, Semantic Models, and Power BI integration patterns. 

• Metadata-Driven Engineering: Build reusable ingestion and transformation frameworks for ERP, CRM, Finance, HR, SharePoint, APIs, databases, files, and external sources. 

• Data Modeling & Analytics Enablement: Create dimensional models, data marts, semantic models, KPIs, and governed reporting layers for self-service BI. 

• Governance, Quality & Security: Define and implement standards for metadata, lineage, auditability, naming conventions, access control, retention, data quality, RBAC, RLS, and compliance. 

• AI-ready Data Foundations: Prepare curated datasets, knowledge repositories, and governed data foundations for AI, GenAI, RAG, forecasting, and advanced analytics use cases. 

• Platform Leadership & Reviews: Conduct design reviews, define engineering standards, mentor engineers, guide implementation teams, and drive reusable documentation. 

• Stakeholder Management: Partner with business stakeholders, architects, and leadership to challenge assumptions, identify opportunities, and propose scalable approaches. 

• Operational Reliability: Optimize datasets, queries, workloads, orchestration, monitoring, release processes, and production SLAs. 

What You'll Bring 

Education 

• Bachelor's/Master's degree in Computer Science, Information Technology, Engineering, Data Science, or equivalent practical experience. 

Experience 

• 9–12 years of experience in Data Engineering, Data Warehousing, BI, Analytics, and enterprise data platform delivery. 

• Minimum 3–5 years of experience designing cloud-based enterprise data platforms or modern lakehouse/warehouse architectures. 

• Proven experience leading large-scale data initiatives from architecture and design through production deployment and support. 

• Experience working with global stakeholders, Sr. Data/Solutions Architects, AI/ML Architects/Engineers, business users, and cross-functional engineering teams. 

Core Technical Skills 

• Data Engineering: Advanced SQL, Python/PySpark, ETL/ELT, API ingestion, incremental processing, monitoring, orchestration, and framework-based delivery. 

• Data Architecture: Lakehouse, EDW, Medallion Architecture, dimensional modeling, star/snowflake schemas, data products, Data Mesh, and enterprise integration patterns. 

• Microsoft Fabric & Azure: Fabric Lakehouse/Warehouse, OneLake, Data Factory, Power BI Semantic Models, Azure Data Lake, Azure SQL, ADF, Service Principals, RBAC, and workspace governance. 

• BI & Semantic Modeling: Power BI, semantic models, tabular modeling, KPI frameworks, executive dashboards, and self-service analytics enablement. 

• Governance & Security: Metadata management, lineage, quality, auditability, RBAC, RLS, privacy, compliance, retention, and governance frameworks. 

• DataOps & DevOps: Git, GitLab/GitHub, Azure DevOps, CI/CD, automated testing, release management, monitoring, and observability. 

Soft Skills 

• Strong ownership mindset with ability to work independently and drive outcomes with minimal supervision. 

• Excellent stakeholder management and communication skills across technical and non-technical audiences. 

• Strong problem-solving, decision-making, prioritization, and ability to manage multiple workstreams. 

• Ability to mentor engineers, guide implementation teams, and balance delivery speed with scalable design. 

Preferred Qualifications 

• Hands-on enterprise experience with Microsoft Fabric Lakehouse, Warehouse, OneLake, Data Factory, and Power BI semantic models. 

• Experience implementing metadata-driven ingestion frameworks and medallion architecture in production. 

• Experience modernizing legacy EDW, ETL, and reporting platforms to cloud-native data platforms. 

• Experience integrating Microsoft 365, SharePoint Online, Microsoft Graph, Teams, and Entra ID with enterprise data platforms. 

• Exposure to AI/ML and GenAI data requirements including RAG-ready repositories and enterprise search foundations. 

• Experience in Energy, Utilities, Industrial IoT, Manufacturing, Engineering, Finance, or Enterprise Operations. 

• Relevant certifications: Azure Data Engineer, Azure Solutions Architect, Microsoft Fabric Analytics Engineer, Snowflake, or Google Cloud certifications.