Solution Architect — Data & AI Platform

Overview

Responsibilities

• Define and drive the overall Data and AI platform architecture using Databricks, Delta Lake, Unity Catalog, and modern lakehouse design patterns.
• Develop scalable architecture patterns that enable AI-ready data products, governed analytics, and enterprise AI use cases.
• Architect secure LLM and Generative AI enablement capabilities, including RAG, embeddings, vector search, semantic search, prompt orchestration, model serving, grounding, and AI evaluation.
• Design agentic AI capabilities that can support data platform operations, including metadata discovery, catalog enrichment, lineage analysis, and data quality investigations.
• Enable AI-assisted documentation generation, pipeline troubleshooting, automated test generation, and operational runbook support.
• Establish architecture standards for governed data products, certified metrics, semantic layers, and consistent business definitions.
• Define approaches for integrating metadata intelligence and semantic context into AI-driven analytics and data experiences.
• Establish secure, scalable, and maintainable patterns for deploying data and AI capabilities across the platform.
• Collaborate with engineering, data, AI, and business stakeholders to translate complex requirements into practical architecture solutions.
• Promote modern engineering practices, including automation and CI/CD, to support reliable delivery and continuous improvement of data and AI platforms.

Requirements

• 10+ years of professional experience in solution architecture, data architecture, platform engineering, or closely related technology roles.
• Strong experience designing modern data platforms, preferably using Databricks, Delta Lake, and Unity Catalog.
• Solid understanding of lakehouse architecture, data governance, metadata management, data products, and semantic modeling.
• Practical knowledge of AI/GenAI architecture patterns, including LLMs, RAG, embeddings, vector search, semantic search, model serving, grounding, and AI evaluation.
• Experience designing or enabling agentic AI workflows, particularly for data discovery, lineage, quality, documentation, troubleshooting, or platform operations.
• Strong SQL skills and familiarity with modern data engineering and analytics ecosystems.
• Experience with CI/CD practices and automation for data and AI platform development and deployment.
• Ability to design secure and scalable architectures that balance innovation, governance, performance, and maintainability.
• Strong understanding of data quality, lineage, metadata, business definitions, and governed metrics.
• Excellent analytical, communication, and stakeholder-management skills, with the ability to explain sophisticated technical concepts to both technical and non-technical audiences.
• Ability to operate at a strategic level while remaining sufficiently hands-on to evaluate technologies, define architecture patterns, and guide implementation teams.

Benefits

• Remote work: Work from a location that supports your productivity and professional needs.
• Contract engagement: Opportunity to contribute to a high-impact Data and AI platform initiative on a project basis.
• Senior-level scope: Take ownership of architecture decisions spanning modern data platforms, AI, governance, and enterprise analytics.
• Cutting-edge technology exposure: Work with Databricks, lakehouse architecture, LLMs, RAG, agentic AI, semantic layers, and metadata intelligence.
• High-impact projects: Help establish foundational capabilities that enable future AI-powered analytics and intelligent data operations.
• Strategic influence: Shape architecture standards and patterns for AI-ready data products and governed enterprise data.
• Collaborative environment: Partner with data, engineering, AI, and business stakeholders on complex technology initiatives.