Snowflake Architect – AWS & Data Governance | Oil & Gas Domain | Houston, TX | Full-Time

Job summary

SNOWFLAKE ARCHITECT - ENERGY VERTICAL Location: Houston, TX(Onsite) Employment Type: Full-Time (Permanent) Compensation: Up to $150,000 per annum PLUS Benefits Interview Process: Two Virtual Rounds and One Onsite Interview JOB DESCRIPTION FOCUS AREAS - Snowflake - AWS - Iceberg - Data Governance - Ontology-Driven Data Models DATA ARCHITECTURE EXPERTISE - Familiarity with data cataloging, governanc

Benefits

Qualifications

• The ideal candidate has strong data engineering fundamentals, experience with enterprise data platforms, and the ability to work with ontologies, semantic models, metadata, and governed data products.
• for security, privacy, access control, performance, and reliability.
• REQUIRED
• Strong communication, documentation, and problem-solving

Responsibilities

• Design, build, and maintain scalable data pipelines across structured, semi-structured, and unstructured data sources.
• Develop data ingestion and extract-load processes using Informatica, aligning with enterprise standards and existing in-house capabilities.
• Build transformation logic using dbt, including modular models, testing, documentation, and deployment workflows.
• Design and manage data structures in Snowflake, with Snowflake positioned as the central strategic data platform.
• Work with AWS-based data services and infrastructure, supporting applications and data products running in the organization//'s AWS environment.
• Support data architecture using Iceberg managed tables, including open table formats, interoperability, cataloging, and governed access patterns.
• Use Snowflake Catalog and Snowflake Horizon to support metadata management, data discovery, governance, lineage, policy enforcement, and trusted data sharing.
• Collaborate with data architects, governance teams, analysts, application teams, and business stakeholders to define trusted data products.
• Work with domain experts to understand business concepts, entities, relationships, and terminology.
• Support ontology-driven modeling, including entity definitions, taxonomies, relationships, business glossaries, and semantic mappings.
• Translate business and domain concepts into logical data models, physical data models, and reusable data products.
• Implement data quality checks, validation rules, lineage, observability, and governance controls.

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