Microsoft Fabric Data Engineer (DE)
Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Aug 12, 2026.
Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.
Our team of 6000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence.
We are a Great Place to Work-Certified? (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ?Best? and ?Fastest Growing? analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine.
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Role Overview:
We are seeking a hands-on Microsoft Fabric Engineer responsible for designing, building, and managing end-to-end data analytics solutions within the unified Microsoft Fabric platform. This role focuses on consolidating data ingestion, transformation, storage, and governance using Fabric-native tools such as Data Pipeline, Data Flow Gen2, Fabric Lakehouse, Fabric Synapse Warehouse & OneLake.
Key Responsibilities:
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Design and implement scalable, metadata-driven frameworks for data ingestion, quality, and transformation across both batch and streaming datasets.
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Develop and optimize end-to-end data pipelines to process structured and unstructured data, enabling the creation of analytical data products.
? Build robust exception handling, logging, and monitoring mechanisms for better observability and operational support. ? Take ownership of complex modules and lead the development of critical data workflows and components. ? Provide guidance to data engineers and peers on best practices.
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Collaborate with cross-functional teams?including business consultants, data architects, scientists, and application developers?to deliver impactful analytics solutions.