Data Engineer Snowflake

 

 

 

 

Job Description

Focus: Snowflake, AWS, Iceberg, Data Governance, and Ontology-Driven Data Models

 

We are looking for a Data Engineer to help design, build, and scale a modern cloud data platform centered on Snowflake and AWS. 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.

This role will support strategic data initiatives using Snowflake, AWS, Iceberg managed tables, Snowflake Catalog, Snowflake Horizon, Informatica, and dbt. The Data Engineer will help create trusted, reusable data assets that support applications, analytics, AI, and business intelligence use cases.

 

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.

 

Strong experience in data engineering, data modeling, ETL/ELT, and cloud data platform development.

Hands-on experience with Snowflake, 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.

 

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.

 

Ideal Candidate Profile: The ideal candidate is a hands-on Data Engineer who can build reliable pipelines and data products while also understanding the meaning and structure of enterprise data. They are comfortable working across Snowflake, AWS, Informatica, dbt, Iceberg, Snowflake Catalog, and Horizon, and can help connect technical implementation with business semantics, ontology, governance, and reusable data strategy.

They understand that data engineering is not only about moving data, but also about making data trusted, discoverable, governed, and meaningful across the enterprise.