AWS Data Architect

As a highly versatile and experienced Technical Architect, your role will involve designing, building, and scaling end-to-end technology solutions across multiple domains including Data Platforms, Application Development, Cloud, Machine Learning, and Generative AI. You are expected to be a multi-domain architect with strong fundamentals and the ability to architect across technologies, drive modernization initiatives, and guide teams in delivering scalable, secure, and high-performance systems. Key Responsibilities: - Enterprise Architecture & Solution Design: - Design end-to-end architectures spanning applications, data platforms, AI/ML systems, and cloud ecosystems. - Define architecture principles, standards, and best practices. - Create HLD/LLD design artifacts. - Ensure solutions are scalable, resilient, secure, and cost-efficient. - Multi-Domain Technology Architecture: - Architect solutions across: 1. Application Development (monoliths, microservices, APIs) 2. Data Platforms (Data Lakes, Lakehouse, Data Warehouses) 3. Cloud-native systems (AWS / Azure / GCP) 4. AI/ML & Generative AI solutions - Enable seamless integration across systems and domains. - Application Architecture & Development: - Design modern application architectures (microservices, event-driven, API-first). - Define integration patterns (REST, GraphQL, messaging, streaming). - Ensure best practices in performance, scalability, and reliability. - Data & Analytics Architecture: - Architect data ecosystems including ingestion, processing, storage, and consumption. - Support batch, streaming, and real-time processing. - Define data modeling, governance, lineage, and quality frameworks. - AI/ML & Generative AI Enablement: - Design and integrate ML and GenAI solutions into enterprise platforms. - Define architectures for: 1. Model lifecycle (training, deployment, monitoring) 2. LLM integration and AI pipelines - Ensure responsible, scalable AI implementations. - Cloud & Platform Engineering: - Architect and implement solutions on AWS / Azure / GCP. - Leverage cloud-native services for applications, data, and AI workloads. - Drive platform engineering, scalability, and cost optimization (FinOps). - Modernization & Transformation: - Lead application and data modernization initiatives: 1. Legacy - Cloud-native 2. Monolith - Microservices 3. Traditional DW - Modern Data Platforms - Define and execute migration strategies (rehost, replatform, refactor, rebuild). - DevOps, Automation & Observability: - Implement CI/CD pipelines across application, data, and ML workflows. - Promote Infrastructure as Code (IaC) and automation. - Define monitoring, logging, and observability frameworks. - Security, Governance & Compliance: - Implement end-to-end security architecture. - Define identity, access control, and data protection mechanisms. - Ensure compliance with enterprise and regulatory standards. - Leadership & Collaboration: - Provide technical leadership and mentorship. - Collaborate with stakeholders, product teams, and engineering teams. - Contribute to solutioning, pre-sales, and innovation initiatives. - Drive adoption of modern engineering and architectural best practices. Preferred Candidate Profile: Required Skills & Experience: - Core Technical Expertise: - Strong experience across: 1. Application Development 2. Data Engineering & Platforms 3. Cloud Architecture - Deep understanding of distributed systems and system design. - Application Development: - Proficiency in Java, Python, or similar languages. - Experience with: 1. Microservices architecture 2. API design and integration 3. Event-driven systems - Data & Analytics: - Experience with: 1. Data Lakes, Data Warehouses, Lakehouse architectures 2. ETL/ELT pipelines and data modeling - Familiarity with big data processing frameworks. - Cloud (Mandatory Any One): - AWS / Azure / GCP with experience in: 1. Application services 2. Data platforms 3. AI/ML services - AI/ML & GenAI (Preferred): - Exposure to: 1. Machine Learning lifecycle and MLOps 2. Generative AI / LLM-based solutions - Understanding of AI integration patterns. - DevOps & Platform Engineering: - Experience with: 1. CI/CD pipelines 2. Infrastructure as Code (Terraform or similar) 3. Containerization (Docker, Kubernetes) Preferred Qualifications: - Experience in modernization and transformation programs. - Exposure to multi-cloud or hybrid architectures. - Certifications in cloud, architecture, or data engineering. - Experience in cost optimization and FinOps. Soft Skills: - Strong architectural thinking and problem-solving. - Excellent communication and stakeholder management. - Ability to translate business requirements into scalable solutions. - Leadership mindset with a focus on mentoring and innovation. As a highly versatile and e