Senior Solution Architect

Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI.

Tiger Analytics is looking for a highly experienced Senior Data & Analytics Solution Architect to lead enterprise-scale modernization and digital transformation initiatives. The ideal candidate will have deep expertise in AWS and Databricks, extensive experience designing enterprise data platforms, and the ability to engage with CXO-level stakeholders to define future-state architectures. This role requires strong consulting, advisory, and technical leadership skills to drive large-scale Data & AI transformation programs.

Key Responsibilities

Lead enterprise Data & Analytics architecture engagements, designing scalable, secure, and future-ready solutions using AWS and Databricks aligned with business objectives.

Assess existing legacy data ecosystems and define target-state architecture, modernization roadmaps, migration strategies, and technology blueprints for enterprise transformation programs.

Act as a trusted advisor to senior client stakeholders (CXO, VP, Director level), conducting architecture workshops, technical discovery sessions, and strategic consulting engagements.

Drive end-to-end solution architecture across cloud data platforms, including data lakes, lakehouses, enterprise data warehouses, streaming platforms, metadata management, governance, and analytics ecosystems.

Define enterprise architecture standards, reusable design patterns, best practices, and governance frameworks while ensuring alignment with organizational technology strategies.

Lead technical solutioning discussions involving clients, implementation partners, and cross-functional delivery teams to ensure architecture consistency and successful program execution.

Design scalable data ingestion, transformation, storage, and analytics architectures supporting structured, semi-structured, and unstructured data.

Collaborate closely with business, product, engineering, and data science teams to translate business requirements into scalable technical solutions.

Recommend platform accelerators, reusable frameworks, automation opportunities, and optimization strategies to improve delivery efficiency and reduce implementation timelines.

Provide architectural oversight throughout project execution, reviewing technical designs, resolving complex technical challenges, and mentoring global engineering teams.

Lead architecture governance by conducting solution reviews, ensuring compliance with enterprise security, scalability, performance, and data governance standards.

Drive cloud modernization initiatives involving migration from legacy data platforms to AWS-based cloud-native architectures.

Evaluate emerging technologies and recommend innovative solutions across Data Engineering, AI/ML, GenAI, and cloud ecosystems.

Support proposal development, solution estimations, RFP responses, technical presentations, and executive-level client discussions.

Requirements

18+ years of IT experience with at least 10 years in Enterprise Data & Analytics Architecture.

Strong expertise in AWS Cloud services including S3, Glue, EMR, Lambda, Redshift, Athena, IAM, Lake Formation, Step Functions, CloudWatch, EventBridge, ECS/EKS, and related services.

Extensive hands-on experience with Databricks , Delta Lake, Unity Catalog, Spark, PySpark, and Lakehouse architecture.

Strong understanding of enterprise Data Warehouse, Data Lake, and Lakehouse architectures.

Expertise in modern data engineering principles, ETL/ELT frameworks, metadata management, data governance, and master data management.

Strong knowledge of SQL, distributed data processing, cloud-native architecture patterns, and scalable data platform design.