Azure Senior Data Architect

Job Summary

• 15+ years of enterprise software engineering and architecture experience with a bachelor’s or master’s degree in computer science, Software Engineering, or a related technical discipline

• Proven track record as a Solution Architect or Enterprise Architect across large-scale, complex programs spanning multiple business domains and technology stacks

• Extensive experience leading digital and AI-driven transformation programs, including stakeholder management.

• Generative AI & Agent Technologies (2+ Years)

  • Hands-on experience architecting and building GenAI Agent solutions using LangChain, LangGraph, CrewAI, AutoGen, or MCP Protocol
  • Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o, Azure OpenAI — including model selection, context management, and cost optimization
  • Experience designing enterprise-grade Retrieval-Augmented Generation (RAG) pipelines, vector stores and knowledge graph integrations
  • Proficiency in prompt engineering techniques: Chain-of-Thought, few-shot/zero-shot strategies, and responsible-AI guardrails
  • Exposure to cloud AI platforms: AWS Bedrock

  Solution & Enterprise Architecture

  • Deep expertise in solution architecture disciplines: capability modelling, application architecture, integration architecture, data architecture, and security architecture
  • Hands-on experience defining target-state architectures, architecture roadmaps, and transition architectures aligned to business strategy
  • Proficiency with architecture frameworks and methodologies: TOGAF, Zachman, or equivalent enterprise architecture frameworks
  • Extensive experience with integration patterns: event-driven architecture (EDA), API-led connectivity, microservices, CQRS, and Saga patterns
  • Strong understanding of cloud architecture principles across AWS, Azure, or GCP — including multi-cloud and hybrid strategies
  • Experience governing architecture across programs: Architecture Review Boards (ARB), design authority participation, and architecture decision records (ADR)

  Technology Stack (Hands-On Proficiency)

  • Java / J2EE: Java 11–21, Spring Boot, Spring Framework, Jakarta EE, microservices with REST/gRPC, Apache Kafka, JPA/Hibernate, PostgreSQL, Oracle
  • .NET: C# (.NET 6/7/8), ASP.NET Core Web API, Entity Framework Core, Azure Service Bus, Dapper, SQL Server, and Cosmos DB
  • Strong ability to design and review polyglot architectures spanning both Java and .NET ecosystems, selecting the right stack for each capability
  • Hands-on experience with API gateway and service mesh technologies: Kong, Apigee, AWS API Gateway, MuleSoft, or Istio
  • Familiarity with frontend frameworks (React, Angular) and mobile patterns sufficient to define end-to-end solution architectures

  DevOps & Platform Engineering (Exposure & Understanding)

  • Good understanding of CI/CD pipeline design using Jenkins, GitHub Actions, GitLab CI, or Azure DevOps — able to define standards and review pipeline architectures
  • Working knowledge of containerization and orchestration: Docker, Kubernetes (EKS, AKS, GKE), Helm, and Istio service mesh
  • Understanding of Infrastructure-as-Code: Terraform, AWS CloudFormation, or AWS CDK for cloud provisioning governance
  • Familiarity with observability stacks: AWS CloudWatch, Prometheus, Grafana, ELK Stack — able to define non-functional requirements and monitoring strategies
  • Appreciation of DevSecOps practices: SAST/DAST tooling, secrets management (HashiCorp Vault, AWS Secrets Manager), and shift-left security in the SDLC

Key Responsibilities

Generative AI & Agent Technologies (2+ Years)

• Hands-on experience architecting and building GenAI Agent solutions using LangChain, LangGraph, CrewAI, AutoGen, or MCP Protocol
• Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o, Azure OpenAI — including model selection, context management, and cost optimization
• Experience designing enterprise-grade Retrieval-Augmented Generation (RAG) pipelines, vector stores and knowledge graph integrations
• Proficiency in prompt engineering techniques: Chain-of-Thought, few-shot/zero-shot strategies, and responsible-AI guardrails
• Exposure to cloud AI platforms: AWS Bedrock

Solution & Enterprise Architecture

• Deep expertise in solution architecture disciplines: capability modelling, application architecture, integration architecture, data architecture, and security architecture
• Hands-on experience defining target-state architectures, architecture roadmaps, and transition architectures aligned to business strategy
• Proficiency with architecture frameworks and methodologies: TOGAF, Zachman, or equivalent enterprise architecture frameworks
• Extensive experience with integration patterns: event-driven architecture (EDA), API-led connectivity, microservices, CQRS, and Saga patterns
• Strong understanding of cloud architecture principles across AWS, Azure, or GCP — including multi-cloud and hybrid strategies
• Experience governing architecture across programs: Architecture Review Boards (ARB), design authority participation, and architecture decision records (ADR)

Technology Stack (Hands-On Proficiency)

• Java / J2EE: Java 11–21, Spring Boot, Spring Framework, Jakarta EE, microservices with REST/gRPC, Apache Kafka, JPA/Hibernate, PostgreSQL, Oracle
• .NET: C# (.NET 6/7/8), ASP.NET Core Web API, Entity Framework Core, Azure Service Bus, Dapper, SQL Server, and Cosmos DB
• Strong ability to design and review polyglot architectures spanning both Java and .NET ecosystems, selecting the right stack for each capability
• Hands-on experience with API gateway and service mesh technologies: Kong, Apigee, AWS API Gateway, MuleSoft, or Istio
• Familiarity with frontend frameworks (React, Angular) and mobile patterns sufficient to define end-to-end solution architectures

DevOps & Platform Engineering (Exposure & Understanding)

• Good understanding of CI/CD pipeline design using Jenkins, GitHub Actions, GitLab CI, or Azure DevOps — able to define standards and review pipeline architectures
• Working knowledge of containerization and orchestration: Docker, Kubernetes (EKS, AKS, GKE), Helm, and Istio service mesh
• Understanding of Infrastructure-as-Code: Terraform, AWS CloudFormation, or AWS CDK for cloud provisioning governance
• Familiarity with observability stacks: AWS CloudWatch, Prometheus, Grafana, ELK Stack — able to define non-functional requirements and monitoring strategies
• Appreciation of DevSecOps practices: SAST/DAST tooling, secrets management (HashiCorp Vault, AWS Secrets Manager), and shift-left security in the SDLC

Skill Requirements

Transformation Program & Stakeholder Management

• Extensive experience leading or playing a senior architecture role within large-scale digital, AI, or cloud transformation programs
• Proven ability to engage and influence senior stakeholders: C-suite executives, program directors, business sponsors, and third-party vendors
• Strong experience translating complex technical architectures into clear business narratives, investment cases, and executive presentations
• Ability to manage architecture across multiple concurrent workstreams, resolving cross-program dependencies and architectural conflicts
• Familiarity with delivery frameworks: SAFe, Agile— able to adapt architecture governance to the delivery model in use

Solution Architecture & SDLC Transformation

• Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance across enterprise programs
• Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices at program scale

Domain Experience

• Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
• Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)

Soft Skills & Professional Competencies

• Excellent written communication skills — ability to produce clear architecture documents, solution proposals, and executive summaries tailored to both technical and non-technical audiences
• Strong verbal communication — able to articulate complex AI and enterprise architecture concepts confidently in design workshops, steering committees, and board-level presentations
• Stakeholder engagement — proven ability to build trusted relationships with business leaders, product owners, delivery teams, and third-party partners to drive architectural alignment
• Collaborative team player — comfortable leading architecture guilds and working across distributed delivery teams while also being self-driven in individual design and analysis
• Mentoring & knowledge sharing — willingness to coach engineers and solution designers, promote architecture best practices, and build technical capability across the organization
• Analytical thinking — structured problem-solver who can navigate ambiguity, assess architectural trade-offs, and propose pragmatic, scalable solutions under program pressure

Adaptability — thrives in fast-moving transformation environments where AI technologies, business priorities, and regulatory requirements evolve rapidly

Other Requirements

1. Microsoft Certified: Azure Solutions Architect Expert (Recommended)