Data Architect

Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce.

If you are passionate about leading large-scale cloud and data modernization initiatives that transform enterprise platforms, we would love to hear from you. We are seeking an experienced and dynamic professional to play a key role in assessing, planning, and driving enterprise migration programs across data, applications, and cloud platforms. This role will help clients modernize their technology landscape, optimize legacy systems, and enable scalable, cloud-native architectures aligned with business goals. The Data Architect will support enterprise data initiatives by designing and delivering scalable, modern data architecture solutions with a primary focus on canonical data modeling, semantic data mesh, and real‑time streaming architectures using Kafka. This role drives alignment across business domains and enables high‑quality, governed, and interoperable data for analytics and operational use cases.

Responsibilities:

• Develop and enhance canonical data models, semantic models, and domain‑driven data contracts to enable cross‑enterprise data interoperability.
• Produce conceptual, logical, and physical data models aligned to business capabilities, modernization goals, and data mesh principles.
• Design and implement data architecture patterns to support real‑time streaming solutions using Kafka.
• Partner with product owners, domain architects, and data engineering teams to translate business needs into scalable, well‑governed data models.
• Ensure consistent application of data governance, metadata management, and lineage standards across data domains.
• Architect and optimize data pipelines and storage solutions using AWS (S3, Redshift, Glue, EMR, Lambda) and Snowflake.
• Contribute architectural artifacts, modeling standards, and reusable templates for enterprise data modeling.
• Support enterprise initiatives around real‑time data integration, semantic mesh, and AI/ML enablement through strong data architecture foundations.