This role is for one of the Weekday's clients

Salary range: Rs 400000 - Rs 3000000 (ie INR 4 - 30 LPA)

Min Experience: 8+ years

Location: India

JobType: full time

We are seeking senior-level professionals with strong hands-on experience in Microsoft Fabric, Python, and Semantic Data Modeling/Ontologies. The ideal candidate will have proven expertise in designing scalable data platforms, building semantic layers, and enabling business insights through modern data engineering practices.

Key Responsibilities

Design, develop, and optimize data solutions using Microsoft Fabric.

Build and manage data pipelines, lakehouses, warehouses, and analytics workloads.

Develop semantic models, ontologies, and knowledge representations to support business use cases.

Implement data engineering solutions using Python and modern data frameworks.

Collaborate with business and technical stakeholders to translate requirements into scalable data architectures.

Ensure data quality, governance, metadata management, and semantic consistency across data assets.

Support advanced analytics, AI/ML,



and enterprise data initiatives.

Required Skills

8+ years of experience in Data Engineering and Analytics.

Strong hands-on expertise in Microsoft Fabric (Data Factory, Lakehouse, Warehouse, Power BI, OneLake).

Experience with IQ/Ontologies, Semantic Modeling, Knowledge Graphs, or related semantic technologies.

Advanced Python programming skills for data processing and automation.

Strong understanding of data architecture, ETL/ELT, data modeling, and cloud-native analytics platforms.

Experience working directly with business stakeholders and enterprise-scale data ecosystems.

Preferred Qualifications

Experience with knowledge graphs, metadata management, and semantic search solutions.

Exposure to AI/GenAI-enabled data platforms.

Microsoft Fabric and Azure certifications preferred.

Must-have skills

MS Fabric, Fabric IQ, Python

Good-to-have skills

Microsoft Fabric, Ontology

📌 Senior Data Engineer lead (India)
🏢 Weekday AI
📍 India