Data & AI Architect

Role Summary:

We are looking for an experienced Data and AI Architect to lead the data platform modernization and AI implementation programs covering discovery, assessment, design and migration strategy for transitioning an On-Premises Data Warehouse platform to a target Modernized On-Premises/Hybrid/Cloud Data Warehouse or Data Lakehouse platform. Additionally, the role will also cover AI use case detailing and implementation using Cloud data and AI platforms. This individual will be responsible for assessing the current environment, proposing a viable migration strategy with high-level architecture blueprints and designing of a target Data lakehouse solution with AI capability overlay. The individual should be capable of evaluating technological options and providing cost-benefit analyses. This individual should also have sufficient knowledge of data ecosystem and AI/ML concepts covering data engineering, data governance, AI model implementation, tune the AI/ML models, training and visualization. The ideal candidate should have over 15+ years of experience in IT, with majority or specialization in data and AI architecture design, solutioning, sizing and migration for on-premises and cloud-based solutions.

Key Responsibilities:

• Discovery & Assessment:
• Conduct a thorough evaluation and assessment of our customers current data warehouse platform, to identify key pain points, challenges, business use case, and opportunities for improvement.
• Assess SQLs, ETLs, cubes and other entities, integrations, consumers, data models and structures that need migration and transformation to move to new lake house.
• Assess use case requirements, data readiness for AI implementation. Identify if it is Gen AI or AI/ML requirement. Identify integration requirements and feasibility.
• Understand customer requirements to adopt and implement the target state.
• Identify security, governance and non-functional requirements.
• Identify data migration requirements and risks if any.
• Design Architecture
• Design Architecture for Data lakehouse on platform chosen for implementation.
• Design AI blueprint architecture with integrations, security and governance requirements.
• Define design decisions, dependencies and policies to be implemented.
• Define environment sizing, integrations and non-functional design decisions.
• Design devops integrated with the overall data platform and AI solution covering tools, configuration, sizing and set up.
• Design code repository and knowledge base structure to be adopted for delivery.
• Lead design walkthrough and seek customer sign off.
• Review the low level design and ensure implementation is aligned to design.
• Present to customer on solution and technical detailing.
• Design Migration Strategy and Solution:
• Design a comprehensive migration strategy that outlines the approach, mapping, data model, cutover strategy, timeline, and potential risks associated with the transition to a target data warehouse or Data Lakes or to new AI model/solution.
• Implementation
• Govern the implementation through configuration, design adoption, testing and integration.
• Develop and Configure the ETLs, transformation logic, integrations
• Unit testing and support UAT
• Cutover strategy implementation and ensuring smooth cutover
• Post production support
• Technology Evaluation:
• Research and evaluate various data and AI platform options, including on-premises, hybrid, and cloud-only solutions, to recommend the most suitable technology alignment with customers business objectives.
• The recommendation of technology should be based on perform cost-benefit analyses and recommend the most viable technology stack with rationale.
• Data Strategy:
• Develop strategies for data acquisition, integration, transformation, pipeline creation, and data mart creation.
• Develop a data strategy that defines the organization’s data assets, data quality standards, and data usage policies.
• Governance & Security:
• Design a robust data governance and security framework to ensure data integrity, confidentiality, and compliance with relevant regulations.
• Stakeholder Communication:
• Collaborate with stakeholders to understand requirements and provide updates.
• Ensure alignment with business objectives and technical needs.
• Project Methodology:
• Should have understanding of project delivery methodologies such as Agile and DevOps to propose Implementation Timeline.

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