Job Description

AI Data & Knowledge Engineer - AVP

About the Role

We are looking for a senior AI Data & Knowledge Engineer to lead the design and evolution of enterprise-grade data and knowledge foundations for AI systems. This role requires deep expertise in data engineering, big data architecture, ETL/ELT design, and scalable backend pipeline development, alongside strong experience in RAG, GraphRAG, ontologies, and knowledge graph technologies. The role will define standards and architecture patterns that ensure AI applications are powered by reliable, governed, and scalable data platforms.

Key Responsibilities

• Lead architecture and technical design for AI-ready data platforms, including ingestion, transformation, storage, serving, and retrieval layers.
• Define standards for scalable batch and streaming data pipelines, ETL/ELT frameworks, and backend data services supporting AI applications.
• Own data and knowledge architecture across the platform, including knowledge graphs, ontologies, semantic models, and retrieval architectures.
• Establish and enforce standards for data contracts, lineage, observability, governance, and quality controls for AI systems.
• Partner with platform, architecture, and AI engineering teams to design robust integration patterns across enterprise source systems and AI services.
• Drive adoption of modern data engineering practices, including orchestration, CI/CD for data pipelines, reusable framework components, and automated quality checks.
• Lead evaluation and implementation of technologies across big data processing, vector search, graph stores, metadata tooling, and AI data infrastructure.
• Coach and develop Officers and Senior Associates; support hiring and capability building across the pod.
• Represent the Data/Knowledge function in architecture reviews, governance forums, and cross-functional planning discussions.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 8–14 years of experience in data engineering, data architecture, platform engineering, or large-scale data systems.
• Deep expertise in building scalable data pipelines, ETL/ELT frameworks, and backend data platforms.
• Strong experience with distributed data processing technologies such as Spark, Databricks, Flink, or equivalent ecosystems.
• Experience designing enterprise data architectures across data lakes, warehouses, lakehouses, and AI-serving layers.
• Strong understanding of orchestration, observability, performance tuning, reliability engineering, and cost-aware pipeline design.
• Deep expertise in RAG, GraphRAG, knowledge graphs, ontology design, semantic modeling, and metadata-driven architectures.
• Experience owning data governance frameworks, data contracts, lineage, and compliance controls at scale.
• Cloud certifications in AWS and/or Azure are a plus.
• Proven experience leading technical teams and influencing architecture decisions across multiple domains.
• Strong written and verbal communication skills, including the ability to present technical designs and trade-offs clearly.

Work Schedule

On-premise

Keywords

Data Engineering, Big Data Architecture, ETL, ELT, Data Pipelines, Spark, Databricks, Kafka, Airflow, Lakehouse, Data Governance, Data Contracts, Metadata, Lineage, RAG, GraphRAG, Knowledge Graphs, Ontology, Vector Database, AI Data Platform

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