Siemens
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Gurugram · On-site · 9-12 Yrs · 11 hours ago
Electronics Manufacturing · 10000+ Employees · Munich, Germany
At Siemens India, we believe in transforming the everyday through innovation, technology, and sustainability. By pioneering digital transformation with AI, IoT, and automation, we optimize efficiency across industries. With our technology, we combine the real and the digital worlds like no other. Bringing together software and hardware to do more with less and accelerate sustainable innovation. Which one of them suits you best?
Hiring: Engineering
What employees say
+ Great Work Environment, Very less work load overall throughout the year
− Management might be clueless about their decisions sometimes, and it backfires sometimes.
More reviews on the company profile.
Interview experience · Process Associate - Finance & Procurement Services
First they screened the resume and shortlisted people received an invitation to walk in interview. The interviews happened in a team lead level. The interview questions ranged from about the person to theoretical questi
Average difficulty · Bengaluru · 1,098 reports
More interview insightsJob description
Overview
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.
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.
Qualifications
- 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.
- 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.
- 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.
- Exposure to AI/ML and GenAI data requirements including RAG-ready repositories and enterprise search foundations.
- Relevant certifications: Azure Data Engineer, Azure Solutions Architect, Microsoft Fabric Analytics Engineer, Snowflake, or Google Cloud certifications.
Nice to have
- Hands-on enterprise experience with Microsoft Fabric Lakehouse, Warehouse, OneLake, Data Factory, and Power BI semantic models.
Education
- Bachelor's/Master's degree in Computer Science, Information Technology, Engineering, Data Science, or equivalent practical experience.
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Job application FAQ
Last updated 17 September 2026
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