Why This Role Stands Out
This hybrid role offers significant growth potential as you'll be instrumental in shaping a strategic data platform, leveraging cutting-edge technologies like Snowflake and dbt. You'll thrive here if you're a proactive individual with a passion for designing robust data solutions and collaborating to deliver impactful data products. Apply now to advance your career in a dynamic environment.
Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
Houston, TX, United States
Posted
2 months ago
AWSSnowflakedbt
Job Description
Job Title: Snowflake Architect
Job Location: Houston, Texas
Job Type: Full-Time
Job Description:
- 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.
- Support data products and applications such as news intelligence, asset intelligence, analytics, and AI-enabled use cases.
- Ensure data solutions meet enterprise requirements for security, privacy, access control, performance, and reliability.
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