Company
Technology
ArchitectDataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Architect Data Engineer. Skills: Data platform architecture, Agentic AI, Knowledge graphs, ETL/ELT pipelines. Define end-to-end architecture. Lead data platform architecture”
What You'll Achieve.
Support real-time agentic execution
Industry & Context.
Performance optimization; Resource optimization; System scalability
What They're Looking For.
Must Have
10+ years data engineering, 10+ years data architecture, 10+ years platform engineering, Architect hybrid data ecosystems, Design knowledge graph, Design ETL/ELT pipelines, Design streaming pipelines, Design batch pipelines, Design CDC pipelines, Understand database internals, Experience building data platforms, Client-facing technical lead, Run workshops, Gather requirements, Define architectural roadmaps, Knowledge of data security principles, RBAC knowledge, Row-level security knowledge, Communication skills, Stakeholder management skills
Nice to Have
Familiarity with semantic layers, Dbt Semantic Layer experience a plus, Cube experience a plus
What You'll Do.
Define end-to-end architecture
Lead data platform architecture
Integrate data systems
Design multi-tenant schemas
Design knowledge graph ontologies
Enable advanced reasoning
Enable contextual understanding
Enable cross-domain data retrieval
Ensure high availability
Serve as technical advisor
Align architectural decisions
Establish performance benchmarks
Design advanced ETL/ELT pipelines
Optimize ETL/ELT pipelines
Define database governance
Enforce database governance
Define scaling strategies
Enforce scaling strategies
Collaborate on API-first design
Collaborate on tool-enabled design
How You'll Work.
Team & Collaboration
Technical advisor for clients; Collaborate on design
Communication Scope
Stakeholder management
Process & Methodology
Roadmap definition
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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