Company
Technology
ArchitectDataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Architect Data Engineer. Skills: Data architecture, Agentic AI, Knowledge graphs, Data platforms. Define and lead end-to-end architecture for data platform. Support Agentic AI”
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
10+ years of experience in data engineering, data architecture, or platform engineering, Architecting hybrid data ecosystems using platforms such as Snowflake, Kinetica, NoSQL, and graph databases, Knowledge of knowledge graph design, including RDF or property graph modeling, Designing and optimizing ETL/ELT pipelines, including streaming, batch, and CDC architectures, Understanding of database internals, including indexing strategies, partitioning, scaling, and performance tuning in cloud environments, Building data platforms that support real-time analytics, AI/ML, or agent-based systems, Client-facing experience as a technical lead, including running workshops, gathering requirements, and defining architectural roadmaps, Knowledge of data security principles such as RBAC and row-level security in distributed systems
Nice to Have
Familiarity with semantic layers (e.g., dbt Semantic Layer, Cube)
What You'll Do.
Define and lead end-to-end architecture for data platform
and graph-based data systems
Design multi-tenant schemas
Design knowledge graph ontologies
Enable advanced reasoning
Enable contextual understanding
Enable cross-domain data retrieval for AI agents
Oversee performance of large-scale data systems
Oversee reliability of large-scale data systems
Oversee security of large-scale data systems
Ensure high availability for mission-critical workloads
Serve as primary technical advisor for clients
Lead discovery workshops
Align architectural decisions with business goals
Align architectural decisions with AI strategy goals
Establish performance benchmarks for data latency
Establish performance benchmarks for retrieval accuracy
Establish performance benchmarks for system scalability
Support real-time agentic execution
Design advanced ETL/ELT pipelines
Optimize advanced ETL/ELT pipelines
Define database governance
Enforce database governance
Define indexing strategies
Define partitioning strategies
Define resource optimization
Define cloud-native scaling strategies
Collaborate on design of API-first data layers
Collaborate on design of tool-enabled data layers
Integrate with AI agents
Integrate with LLM-based systems
How You'll Work.
Team & Collaboration
Cross-functional teams; AI and data engineering teams
Communication Scope
Stakeholder management; Consultative mindset
Process & Methodology
Architectural roadmaps
Full Job Description
## Accountabilities Define and lead the end-to-end architecture for a modern data platform supporting Agentic AI, integrating structured, unstructured, and graph-based data systems. Design multi-tenant schemas and knowledge graph ontologies to enable advanced reasoning, contextual understanding, and cross-domain data retrieval for AI agents. Oversee performance, reliability, and security of large-scale data systems including Snowflake and Kinetica, ensuring high availability for mission-critical workloads. Serve as the primary technical advisor for clients, leading discovery workshops and aligning architectural decisions with business and AI strategy goals. Establish performance benchmarks for data latency, retrieval accuracy, and system scalability to support real-time agentic execution. Design and optimize advanced ETL/ELT pipelines, including streaming, batch, and CDC-based data ingestion strategies. Define and enforce database governance, including indexing, partitioning, resource optimization, and cloud-native scaling strategies. Collaborate on the design of API-first and tool-enabled data layers for integration with AI agents and LLM-based systems. Requirements: 10+ years of experience in data engineering, data architecture, or platform engineering roles within enterprise or AI-driven environments. Strong expertise in architecting hybrid data ecosystems using platforms such as Snowflake, Kinetica, NoSQL, and graph databases. Deep knowledge of knowledge graph design, including RDF or property graph modeling for enterprise-scale systems. Proven experience designing and optimizing ETL/ELT pipelines, including streaming, batch, and CDC architectures. Strong understanding of database internals, including indexing strategies, partitioning, scaling, and performance tuning in cloud environments. Experience building data platforms that support real-time analytics, AI/ML, or agent-based systems. Client-facing experience as a technical lead, including running workshops,
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