Senior Engineer - Data & AI

About the Role:

We are seeking a Techno-Functional Data Architect who can engage with business stakeholders, understand functional needs, and translate them into scalable data architecture and AI-enabled solution designs. The role requires strong working knowledge of Databricks on AWS, data Lakehouse architecture, data modelling, governance, and analytics engineering. The individual will partner with business, product, and engineering teams to shape solution blueprints, validate ideas through prototypes, define delivery-ready architecture, and ensure data solutions are secure, scalable, reusable, and aligned with enterprise standards.

Key Responsibilities

• Business Engagement & Functional Translation
• Engage with business stakeholders to understand processes, pain points, data needs, and decision-making use cases.
• Translate functional requirements into data architecture, solution design, and delivery-ready user stories.
• Facilitate discovery workshops with business SMEs, product owners, architects, and engineering teams.
• Define measurable business outcomes and ensure solution designs remain aligned with business value.
• Communicate architecture decisions and technical trade-offs in clear, business-friendly language.
• Data Architecture & Solution Blueprinting
•  Design logical and conceptual data architecture for analytics, AI, and enterprise data products.
• Define Lakehouse patterns using Databricks, Delta Lake, Unity Catalog, and AWS data services.
• Create source-to-consumption solution blueprints covering ingestion, transformation, governance, serving, and analytics layers.
• Guide data modelling, metadata, lineage, access control, and quality design considerations.
• Assess scalability, security, maintainability, and reusability of proposed data solutions.
• Prototyping, AI Enablement & Delivery Advisory
• Build or guide rapid prototypes to validate data, analytics, and AI solution concepts.
• Evaluate feasibility of GenAI, RAG, ML, and intelligent automation use cases on the data platform.
• Convert stakeholder feedback from prototypes into scalable architecture and delivery recommendations.
• Collaborate with engineering teams to ensure architectural intent is correctly implemented.
• Prepare concise functional, architectural, and technical documentation for approved solutions.
• Data Engineering Oversight & Platform Best Practices
• Act as the primary liaison between business and technical teams.
• Provide guidance on Databricks, PySpark, SQL, Delta Lake, and workflow design best practices.
• Review data pipelines, transformation patterns, and data products for performance and maintainability.
• Support design of reusable components, data quality rules, and platform guardrails.
• Align implementation approach with enterprise data governance and security expectations.
• Enable dashboarding, reporting, and advanced analytics through well-structured data assets.
• Stakeholder Communication & Collaboration
• Present solutions, prototypes, and recommendations to business users.
• Facilitate discussions between Product Owners, Business SMEs, Architects, and Engineering teams.
• Participate in Agile ceremonies including sprint planning, demos, and retrospectives.
• Support user acceptance testing and business sign-offs.

Ideal Candidate Experience

• 2-5 years of experience in data architecture, data engineering, analytics, or Data & AI solution design.
• Strong exposure to Databricks on AWS, Python, PySpark, SQL, Delta Lake, and data modelling concepts.
• Comfortable working directly with business users to understand requirements and shape architecture-led solutions.
• Able to create architecture views, solution blueprints, data flows, and concise technical documentation.
• Able to guide prototypes and translate stakeholder feedback into scalable delivery recommendations.
• Aspires to grow into a Data Architect, Solution Architect, AI Solution Lead, or Enterprise Data Platform Consultant role.