Who we are
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 4000+ 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.
Curious about the role? What your typical day would look like?
Role Overview:
We are seeking street-smart and technically strong Senior Data Engineers / Leads who can take ownership of designing and developing cutting-edge data and AI platforms using Azure-native technologies and Databricks. You will play a critical role in building scalable data pipelines, modern data architectures, and intelligent analytics solutions.
Key Responsibilities:
- Design and implement scalable, metadata-driven frameworks for data ingestion, quality, and transformation across both batch and streaming datasets.
- 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.
- Collaborate with cross-functional teams—including business consultants, data architects & scientists, and application developers—to deliver impactful analytics solutions.