Lead Data Engineer / Data Architect

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.

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.

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