Snowflake Architect

Tavant Technologies•Houston, TX

About The Position

We are looking for a hands-on Data Engineer to help design, build, and scale a modern cloud data platform centered on Snowflake and AWS. The role supports strategic data initiatives using Snowflake, AWS, Iceberg managed tables, Snowflake Catalog, Snowflake Horizon, Informatica, and dbt. It focuses on creating trusted, reusable data assets for applications, analytics, AI, and business intelligence. The position combines data engineering and data modeling with data governance and business semantics. The successful candidate will build reliable pipelines and data products while helping translate business concepts, entities, relationships, and terminology into governed, discoverable, and reusable data models and products. Data solutions must meet enterprise requirements for security, privacy, access control, performance, and reliability.

Requirements

Strong experience in data engineering, data modeling, ETL/ELT, and cloud data platform development.
Hands-on Snowflake experience, including data modeling, performance optimization, access controls, and scalable warehouse/lakehouse patterns.
Experience working in AWS cloud environments.
Experience with Informatica or similar enterprise data integration platforms for extract-load and ingestion patterns.
Experience with dbt for data transformations, testing, documentation, and analytics engineering workflows.
Understanding of Apache Iceberg or open table formats, including managed tables, schema evolution, interoperability, and catalog-based access.
Familiarity with data cataloging, governance, lineage, metadata management, and policy-driven data access.
Understanding of ontology, semantic modeling, taxonomies, business glossaries, or knowledge graph concepts.
Strong SQL skills and experience with Python or another data engineering language.
Ability to work with business stakeholders to define data entities, relationships, metrics, and data product requirements.
Strong communication, documentation, and problem-solving skills.

Nice To Haves

Experience with Snowflake Catalog and Snowflake Horizon.
Experience building governed data products for analytics, AI, or application use cases.
Experience with enterprise governance tooling, especially Informatica governance capabilities.
Experience with RDF, OWL, SHACL, SPARQL, graph databases, or knowledge graph platforms.
Experience designing semantic layers, business glossaries, metadata models, or domain ontologies.
Experience with CI/CD, Git, automated testing, and deployment workflows for data pipelines.
Experience with data observability, lineage tracking, data contracts, and data quality frameworks.
Experience working in complex enterprise data environments with multiple systems, domains, and stakeholder groups.

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, aligned 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, the organization’s central strategic data platform.
Work with AWS-based data services and infrastructure supporting applications and data products 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 and 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.