Sr. Data Architect  

Role Overview
We are seeking a Data Architect to design, implement, and evolve modern data platform architectures across cloud, hybrid, and enterprise environments. You will work closely with business stakeholders, data engineers, platform teams, and solution architects to translate requirements into scalable and secure architectural designs that enable analytics, governance, and enterprise decision-making.

Key Responsibilities

1. Architecture Design

• Lead the design of modern data architectures including data lakes, warehouses, and lakehouses.
• Define data integration patterns, ingestion pipelines, ETL/ELT frameworks, and real-time/streaming architectures.
• Develop logical, physical, and conceptual data models aligned with business and analytics requirements.
• Ensure scalability, performance, reliability, and cost-efficiency of data platforms.

2. Platform & Solution Design

• Design data solutions across on-prem, hybrid and cloud platforms.
• Define storage, compute, security, lineage, and metadata frameworks.
• Recommend technology options, architecture decisions, and best practices.
• Ensure alignment with enterprise architecture principles and standards.

3. Data Governance & Security

• Implement data governance principles and metadata management frameworks.
• Design access, lineage, cataloging, data quality, and compliance frameworks.
• Ensure alignment to enterprise security, regulatory, and industry standards.

4. Delivery & Technical Leadership

• Work closely with data engineers and delivery teams to ensure successful solution implementation.
• Provide technical leadership during development, integration, and deployment.
• Conduct architecture reviews, design validation, and performance optimisation.
• Produce high-quality architecture documentation, diagrams, and technical specifications.

5. AI Architecture

• Experience in the following areas is highly advantageous:
• AI and ML architecture patterns
• LLMs, RAG, or GenAI solutions
• Data pipelines for AI workloads
• ModelOps and AI lifecycle considerations
• GPU compute and AI infrastructure
  

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related fields.
• 5–12 years of experience in data architecture, data platform engineering, or data solution design.
• Strong hands-on experience in modern data platforms (cloud, lakehouse, distributed data).
• Strong understanding of ETL/ELT, data modeling, data governance, and security.
• Experience in consulting, or system integration environments is preferred.
• Experience with AI architecture is a strong advantage but not mandatory.

Skills

• Modern cloud data platforms
• Data lake / warehouse / lakehouse
• ETL / ELT & integration pipelines
• Data modeling and metadata
• Data governance & security

Personal Attributes

• Strong analytical and problem-solving ability.
• Excellent communication and documentation skills.
• Ability to collaborate across multidisciplinary teams.
• Self-driven, structured, and detail-oriented.
• Passion for modern data platforms and emerging technologies.