Data & Context Architect

Function

Cloud & Data Engineering

Our Company

We’re Hitachi Digital Services, a global digital solutions and transformation business with a bold vision of our world’s potential. We’re people-centric and here to power good. Every day, we future-proof urban spaces, conserve natural resources, protect rainforests, and save lives. This is a world where innovation, technology, and deep expertise come together to take our company and customers from what’s now to what’s next. We make it happen through the power of acceleration.

Imagine the sheer breadth of talent it takes to bring a better tomorrow closer to today. We don’t expect you to ‘fit’ every requirement – your life experience, character, perspective, and passion for achieving great things in the world are equally as important to us.

Job description

Role Summary

We are seeking a Data and Context Architect to design the semantic, knowledge, and governance layers that enable AI agents to operate on trusted, well-structured, and governed data assets.

Key Responsibilities

• Design and develop the metrics layer, ontology framework, search indexes, and business glossary. • Establish and evaluate data readiness across dimensions such as scope, freshness, semantic clarity, quality, governance, usability, and operational effectiveness. • Elicit business rules from SMEs and translate them into reusable, governed, and scalable models. • Define entitlement frameworks, data lineage standards, and agent identity requirements.

Required Technical Skills

• Expertise with lakehouse and cloud-based data platforms, preferably Databricks, including Delta Lake, Unity Catalog, PySpark, or comparable technologies. • Strong knowledge of semantic and metrics layers, dimensional modeling, and domain-driven data modeling. • Hands-on experience with ontologies and knowledge graphs, including RDF/OWL, Neo4j, or similar property graph technologies. • Experience with vector databases, semantic search, embeddings, and Retrieval-Augmented Generation (RAG) architectures. • Deep understanding of data governance disciplines, including data lineage, access management, business glossaries, and master data management. • Experience with event-driven architectures and business rules implementation using technologies such as Kafka, Amazon MSK, alerting platforms, and rules engines. • Proficiency in data quality management, data reconciliation, and data readiness assessments.