Role Overview
We are seeking an exceptional Data Architect / Lead Data Engineer who combines deep architectural thinking with strong hands-on engineering capabilities.
This is not a documentation-only architecture role. The ideal candidate can define target architectures, make critical technology decisions, design reusable patterns, and work directly with engineering teams to build and deliver production-grade solutions.
You will help design and implement modern enterprise data platforms, integration frameworks, governed data products, semantic layers, knowledge graphs, GraphRAG solutions, and AI-ready data foundations.
You will work closely with clients, enterprise architects, AI engineers, product teams, and delivery leaders while contributing to technical standards, delivery methodologies, reusable assets, and engineering culture.
Key Responsibilities
- Design end-to-end enterprise data architectures.
- Define current, transition, and target-state architectures.
- Design modern data platforms, including warehouses, lakes, lakehouses, mesh, and fabric.
- Build scalable ingestion, integration, transformation, orchestration, and activation pipelines.
- Define ETL/ELT, API, CDC, and streaming integration patterns.
- Design governed data products.
- Develop conceptual, logical, physical, canonical, and semantic data models.
- Build semantic layers, ontologies, knowledge graphs, GraphRAG solutions, and vector stores.
- Establish metadata, lineage, governance, observability, security, and compliance.
- Support analytics, ML, GenAI, and intelligent-agent initiatives.
- Create reference architectures and engineering standards.
- Conduct architecture reviews and optimization.
- Partner directly with clients and stakeholders.
- Mentor engineers and contribute hands-on to delivery.
Required Experience and Skills
- 10–15+ years of experience in data architecture and/or data engineering.
- Strong combination of architecture expertise and hands-on engineering.
- Experience delivering enterprise-scale solutions.
- Deep understanding of the end-to-end data lifecycle.
- Strong data modeling expertise.
- Experience with ETL, ELT, CDC, APIs, and streaming.
- Experience with cloud and data platforms such as Snowflake, Databricks, AWS, Azure, and Google Cloud Platform.
- Strong understanding of data warehouses, lakehouses, data mesh, and data fabric.
- Experience with data governance, metadata, lineage, and data quality.
- Experience designing data products and data contracts.
- Experience building analytics and AI-ready data foundations.
- Excellent communication and stakeholder-management skills.
- Ability to explain complex technical concepts clearly.
- AI, Semantic & Context Engineering Experience
- Experience with LLMs, GenAI, and intelligent agents.
- RAG, GraphRAG, knowledge graphs, and vector databases.
- Ontologies and semantic modeling.
- Metadata-driven automation.
- AI evaluation and responsible AI practices.
- Enterprise AI integrations.
- AI-enabled data quality and lineage.
- AI-Native Engineering Skills
- Experience with AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, Windsurf, OpenAI, and Gemini.
- Familiarity with agentic frameworks.
- Understanding of Model Context Protocol (MCP).
- AI-assisted software and data engineering.
- Prompt engineering and workflow automation.
- Experience implementing AI with human validation and governance.