Senior Data Architect / Data Engineer AI/ML

Context66 is a Data, AI, and Enterprise Architecture services company focused on engineering the context that powers AI. They are seeking an exceptional Senior Data Architect / Lead Data Engineer who will design and implement modern enterprise data platforms and work closely with various teams to deliver production-grade solutions.

Responsibilities:

• Design end-to-end enterprise data architectures across structured, unstructured, streaming, batch, and event-driven data
• Define current-state, transition-state, and target-state architectures aligned with business outcomes
• Design and implement modern data platforms, including data warehouses, data lakes, lakehouses, data mesh, data fabric, and federated data architectures
• Build scalable data ingestion, integration, transformation, orchestration, and activation pipelines
• Define integration patterns across ETL, ELT, APIs, CDC, messaging, streaming, micro-batch, virtualization, and zero-copy architectures
• Design reusable, governed data products with clear ownership, contracts, SLAs, quality expectations, and lifecycle controls
• Develop canonical, conceptual, logical, physical, and semantic data models
• Design enterprise semantic layers, metric definitions, business entities, ontologies, and reusable context models
• Build and integrate knowledge graphs, graph databases, GraphRAG solutions, vector stores, and retrieval architectures
• Establish patterns for metadata management, lineage, data quality, observability, governance, security, privacy, and compliance
• Design data and feature pipelines that support analytics, machine learning, Generative AI, and intelligent agent use cases
• Create reference architectures, technical standards, reusable frameworks, accelerators, and engineering playbooks
• Perform architecture reviews, design validation, code reviews, performance optimization, and technical risk assessments
• Partner with client stakeholders to lead discovery, translate business requirements, and communicate architecture decisions clearly
• Mentor engineers and architects while raising the technical quality and delivery maturity of the team
• Contribute directly to prototypes, pilots, production implementations, and complex troubleshooting when needed

Requirements:

• 10 to 15+ years of experience across data architecture, data engineering, integration, analytics, and enterprise platforms
• Strong combination of strategic data architecture and hands-on engineering expertise
• Demonstrated experience designing and delivering large-scale, production-grade enterprise data solutions
• Deep understanding of the complete data lifecycle, from creation and ingestion through processing, consumption, retention, archival, and deletion
• Strong expertise in data modeling, including conceptual, logical, physical, canonical, and semantic models
• Extensive experience with ETL, ELT, CDC, APIs, event-driven integration, streaming, orchestration, and data activation
• Hands-on experience with modern cloud data platforms such as Snowflake, Databricks, AWS, Azure, or Google Cloud
• Strong knowledge of data warehousing, lakehouse architectures, data lakes, data mesh, data fabric, virtualization, and federated query patterns
• Practical experience with data governance, metadata, lineage, quality, observability, master data, access controls, and regulatory requirements
• Deep understanding of reusable data products, data contracts, domain ownership, SLAs, and federated governance
• Strong knowledge of analytics, BI, self-service data consumption, feature stores, and AI-ready data foundations
• Excellent problem-solving, communication, documentation, and stakeholder-management skills
• Ability to explain complex technical concepts clearly to both executives and engineering teams
• Large language models, Generative AI, intelligent agents, and enterprise AI architectures
• RAG, GraphRAG, knowledge graphs, graph databases, and vector databases
• Ontologies, semantic modeling, enterprise semantic layers, and metric frameworks
• Metadata-driven automation, active metadata, policy enforcement, and context engineering
• AI evaluation, observability, grounding, hallucination reduction, security, and responsible AI controls
• Integration of enterprise data platforms with intelligent applications and agent workflows
• AI-enabled data quality, metadata enrichment, lineage discovery, and engineering automation
• Claude Code, Cursor, GitHub Copilot, Windsurf, OpenAI, Gemini, or equivalent AI-assisted development tools
• Agentic development frameworks and AI orchestration platforms
• Model Context Protocol architectures and tool-enabled agent patterns
• AI-assisted software engineering, testing, documentation, data modeling, and code generation
• Autonomous agents, workflow automation, prompt engineering, and structured output patterns
• Using AI to accelerate delivery while maintaining human validation, security, governance, and engineering rigor
• Comfortable operating in an early-stage, high-growth environment
• Able to work independently, move quickly, and deliver with limited supervision
• Willing to move between architecture, engineering, client discussions, and delivery execution
• Strong sense of ownership, accountability, urgency, and attention to quality
• Customer-first mindset with a focus on measurable outcomes
• Comfortable challenging assumptions and proposing better technical approaches
• Passionate about learning, experimentation, innovation, and emerging technologies
• Able to mentor others while remaining hands-on when the situation requires it
• Interested in helping build a company, not only completing assigned project tasks
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline
• Relevant cloud, data platform, architecture, graph technology, or AI certifications are beneficial