Data Architect

CareerUS Solutions; Houston (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

CareerUS Solutions is seeking an experienced Data Engineer / Data Architect to design, build, and scale a modern cloud data platform using Snowflake and AWS. The role emphasizes data governance, metadata, semantic models, ontologies, and governed data products.

The project involves working with Snowflake, AWS, Iceberg, Snowflake Catalog and Horizon, Informatica, and dbt to create trusted analytics-ready data assets for AI and business intelligence, with attention to security and reliability.

Qualifications

• Experience in data engineering and data modeling.
• ETL/ELT and cloud data platform development.
• Python or a data engineering language.

Responsibilities

• Design, build, and maintain scalable data pipelines.
• Ingest and load data using Informatica or similar.
• Create dbt transformation models and tests.
• Manage data structures in Snowflake.
• Support Snowflake Catalog and Horizon governance.
• Ensure data quality, lineage, and security.

Skills

Data modeling

ETL/ELT

Python

Tools

Snowflake

dbt

Informatica

AWS

Iceberg

Job description

Snowflake | AWS | Apache Iceberg | Data Governance | Ontology / Semantic Data Models

Role Summary

We are looking for an experienced Data Engineer / Data Architect to design, build, and scale a modern cloud data platform using Snowflake and AWS.

The ideal candidate should have strong data engineering and architecture experience, along with knowledge of data governance, metadata, semantic models, ontologies, and governed data products.

The role will work with technologies including Snowflake, AWS, Iceberg, Snowflake Catalog, Snowflake Horizon, Informatica, and dbt to create trusted and reusable data assets for analytics, AI, applications, and business intelligence.

Key Responsibilities

• Design, build, and maintain scalable data pipelines across structured, semi-structured, and unstructured data.
• Develop ingestion and extract-load processes using Informatica or similar platforms.
• Build transformation models using dbt, including testing, documentation, and deployment.
• Design and manage data structures in Snowflake.
• Work with AWS data services and infrastructure.
• Support Apache Iceberg managed tables, open table formats, cataloging, and governed access.
• Use Snowflake Catalog and Horizon for metadata, discovery, lineage, governance, and policy enforcement.
• Work with architects, governance teams, analysts, application teams, and business stakeholders.
• Define business entities, relationships, terminology, and semantic mappings.
• Support ontology-driven data modeling, taxonomies, business glossaries, and semantic layers.
• Translate business concepts into logical and physical data models and reusable data products.
• Implement data quality, validation, lineage, observability, and governance controls.
• Support data products for analytics, AI, applications, and intelligence use cases.
• Ensure security, privacy, access control, performance, and reliability requirements are met.

Required Skills

Data Engineering & Architecture

• Strong data engineering and data modeling experience.
• ETL/ELT and cloud data platform development.
• Python or another data engineering language.

Snowflake

• Hands-on Snowflake experience.
• Data modeling and performance optimization.
• Snowflake security and access controls.
• Scalable warehouse/lakehouse architecture.

Cloud

• Strong experience with AWS.
• Experience with services such as S3, Glue, Lambda, Athena, EMR, and Redshift.

Data Integration & Transformation

• Informatica
• dbt
• Schema evolution and interoperability.
• Data cataloging and metadata management.
• Data lineage and data quality.
• Governance and policy-based data access.

Ontology / Semantic Modeling

• Ontologies and semantic modeling.
• Taxonomies and business glossaries.
• Entity and relationship modeling.
• Knowledge graph concepts.

Preferred Skills

• Snowflake Catalog and Snowflake Horizon.
• Governed data products for AI and analytics.
• RDF, OWL, SHACL, SPARQL.
• Graph databases / knowledge graph platforms.
• Semantic layers and metadata models.
• CI/CD, Git, automated testing, and deployment.
• Data observability and data contracts.
• Experience in complex enterprise data environments.