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 Lang Chain, Lang Graph, CrewAI, Auto Gen, 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, Mule Soft, 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 Dev Sec Ops practices: SAST/DAST tooling, secrets management (Hashi Corp 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 Lang Chain, Lang Graph, CrewAI, Auto Gen, 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, Mule Soft, 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 Dev Sec Ops practices: SAST/DAST tooling, secrets management (Hashi Corp 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 Dev Sec Ops 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)