Data Architect

Introduction

The role involves preparing the Strategic Process Inventory (SPI) data estate for use by the GenAI platform. The responsibilities include defining the data onboarding approach, identifying required source structures, designing performant access patterns, and ensuring metadata is complete, accurate, and usable by downstream semantic and AI context layers.

Required Skills & Qualifications

• 8 years of experience in enterprise data architecture, database architecture, or data platform architecture.
• Strong experience with Oracle relational databases and enterprise data models.
• Experience designing performant data access patterns for analytics, reporting, or AI-enabled query systems.
• Experience with metadata onboarding, data catalogs, business glossaries, and semantic modeling.
• Advanced SQL, especially Oracle SQL.
• Strong understanding of PL/SQL concepts.
• Working knowledge of Python for metadata extraction, profiling, or automation is preferred.
• Familiarity with YAML / JSON metadata formats is useful.
• Prior work experience at client or in client's Industry.

Applicants must be able to work directly for Artech on W2.

Preferred Skills & Qualifications

• Experience with enterprise process management systems (POP, ARIS).
• Exposure to GenAI platforms and LLM-based enterprise solutions.
• Knowledge of regulatory and compliance-driven data environments.
• Experience with Airflow, dbt, Spark, or enterprise data pipeline tooling.

Day-to-Day Responsibilities

• Identify and document the required SPI Oracle database schemas, tables, views, columns, keys, relationships, and constraints needed for GenAI.
• Define the onboarding scope for structured data, including source systems, subject areas, entities, and key data products.
• Extract, validate, and organize technical metadata from Oracle using tools such as Toad, SQL scripts, catalog exports, or database dictionary queries.
• Establish metadata standards for table descriptions, column definitions, data types, keys, data classifications, and business relevance.
• Work with business and data SMEs to validate whether the onboarded data accurately represents the enterprise process management business domain.
• Analyze SPI data structures to determine where direct source access may be inefficient for natural-language query workloads.
• Design optimized database views or materialized views to improve performance for GenAI query execution.
• Define aggregation, denormalization, join, and filtering strategies for commonly used SPI data access paths.
• Ensure materialized views preserve semantic correctness and do not introduce ambiguity into business reporting.
• Define refresh frequency, dependency management, and operational ownership for materialized views.
• Provide canonical source-to-target mapping between Oracle data structures and business concepts.
• Support the Knowledge / Data Engineer in mapping business terminology to physical Oracle tables and columns.
• Identify authoritative source fields for core SPI business terms, metrics, dimensions, and identifiers.
• Define reusable data access patterns that can be reflected in the semantic layer and AI context artifacts.
• Validate data quality, completeness, referential consistency, and metadata accuracy.
• Identify sensitive, restricted, or regulated data elements that require access controls or masking.
• Document known data limitations, latency, interpretation caveats, and usage constraints.
• Ensure data onboarding artifacts are versioned and traceable.

For immediate consideration please click APPLY to begin the screening process with Alex.