Lead Data Engineer / Data Architect

Job description

**About the Role** We are looking for a highly experienced Lead Data Engineer / Data Architect to **own the design, implementation**, governance, and delivery of enterprise data platforms supporting analytics**, reporting, AI**, and operational excellence initiatives across Digital Grid. This is not a developer-only role. The person should combine **architecture thinking with hands-on implementation** and provide technical leadership across multiple parallel initiatives. This role is suited for a **self-driven technical leader** who can **independently** own workstreams, engage stakeholders, translate business requirements into scalable technical solutions, and **drive delivery with a high degree of autonomy.** **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.**