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Data Architect - W2

Congensys Corp.Plano, TX🇺🇸United StatesPosted Sep 23, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Plano, TX, United States
Posted
19 hours ago
OraclePL/SQLSQLGitPython

Job Description

Data Architect

Plano, TX- Hybrid role
only local as F2F required

$70 pr hr on W2

W2 Candidates –Visa open
H1b Transfer is also fine

 

Main Skills:

  • Data Architecture
  • Data Modeling
  • Metadata
  • PL/SQL
  • Materialized Views/Designing

 

As a Data Architect, you will:

Required 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.
  • Data and Metadata Onboarding
  • 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.
  • Toad for Oracle or equivalent Oracle metadata extraction tools.
  • Data catalog or metadata management platforms.
  • Git or enterprise source control.
  • CI/CD awareness for database scripts and metadata assets.
  • 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.

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