Senior Solution Architect

Job Summary

We are looking for an experienced Data & AI Solution Architect to drive the design and implementation of modern data, analytics, and AI solutions across the enterprise. In this role, you will work closely with business and technology stakeholders to define scalable architectures, modernize data platforms, and enable AI-driven capabilities that deliver measurable business value.

The ideal candidate brings strong experience in cloud data platforms, data engineering, and enterprise architecture, along with hands-on expertise in Generative AI, LLM-based applications, and agentic AI solutions. You will provide technical leadership across data and AI initiatives, guide engineering teams on best practices, and help shape the organization's data and AI strategy. Experience with platforms such as Databricks, Snowflake, Azure, AWS, or GCP, combined with a solid understanding of data governance, security, and modern lakehouse architectures, will be critical to success in this role.

This position requires a balance of strategic thinking and hands-on technical leadership, along with the ability to communicate complex architectural concepts effectively to senior business and executive stakeholders.

Key Responsibilities

Define and lead the architecture, design, and implementation of enterprise-scale Data & AI solutions on cloud platforms such as Azure, AWS, and GCP.

Design modern data platforms leveraging Databricks, Snowflake, data lakes, lakehouses, and cloud-native analytics services.

Partner with business and technology stakeholders to translate business requirements into scalable, secure, and future-ready data and AI architectures.

Lead the development of Generative AI, LLM-powered applications, AI agents, and RAG-based solutions to address complex business challenges.

Evaluate and implement vector databases, agent frameworks, and AI orchestration platforms to support advanced AI use cases.

Establish data modelling standards and guide teams on conceptual, logical, and physical data design best practices.

Define data integration strategies and oversee the implementation of ETL/ELT pipelines using modern data engineering frameworks and tools.

Ensure solutions adhere to enterprise standards for data governance, security, privacy, compliance, and risk management.

Provide technical leadership, architecture governance, and design reviews across multiple data and AI initiatives.

Collaborate with engineering, platform, and operations teams to ensure successful solution delivery and adoption.

Drive innovation by evaluating emerging technologies, industry trends, and best practices in Data, Analytics, AI, and Agentic AI.

Mentor architects, engineers, and technical teams, fostering a culture of engineering excellence and continuous learning.

Support presales activities, solution proposals, effort estimations, and technical discussions with customers and partners.

Present architecture recommendations, AI strategies, and transformation roadmaps to senior leadership and executive stakeholders.

Contribute to the establishment of enterprise reference architectures, reusable frameworks, and technology standards across the organization.

Skill Requirements

Required Skills & Experience

• 14+ years of overall experience in data architecture, data engineering, or related roles.
• 8+ years of hands-on experience with cloud data platforms (AWS, Azure, and/or GCP) - architecture, design, and implementation.
• Hands-on experience with Databricks and/or Snowflake for large-scale data engineering, analytics, and lakehouse workloads.
• Proven hands-on experience with AI/ML solutions, including LLM-based application development and AI agent design/development.
• Strong grounding in data modelling (conceptual, logical, physical), data warehousing, and modern lakehouse architectures.
• Experience with data integration tools/ETL-ELT frameworks (e.g., Informatica, dbt, Talend, Glue, ADF).
• Working knowledge of vector databases (e.g., Pinecone, FAISS, pgvector) and RAG architectures.
• Experience with agent frameworks/protocols (e.g., LangChain, LangGraph, AutoGen, MCP) is highly desirable.
• Proficiency in SQL and at least one programming language (Python preferred) for prototyping and integration work.
• Solid understanding of data governance, security, and privacy/compliance requirements (e.g., GDPR).
• Strong stakeholder management, communication, and technical leadership skills.

Preferred Qualifications

• Cloud certifications (AWS/Azure/GCP Solutions Architect or Data Engineer).
• Databricks (e.g., Databricks Certified Data Engineer) and/or Snowflake (e.g., SnowPro Core) certifications.
• Experience in a specific domain relevant to the business (e.g., banking, financial services, insurance).
• Exposure to MLOps/LLMOps tooling (MLflow, Vertex AI, SageMaker, Azure AI Studio).
• Experience presenting architecture and AI strategy to senior/executive stakeholders.

Other Requirements

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