What are the responsibilities and job description for the Snowflake Architect or Data Architect position at MetaSense, Inc.?
Must-Have Requirements:
1) Strong Snowflake Data Architecture experience – Data modeling, data warehouse/lake house design, performance optimization, security, access controls, and enterprise-scale Snowflake implementations.
2) Data Governance & Metadata Management expertise – Hands-on experience with data cataloging, metadata management, data lineage, data quality, governance frameworks, and policy-driven data access.
3) Understanding of Ontology & Semantic Modeling 4) Working knowledge of AWS services such as S3, Glue, Lambda, Athena, EMR, Redshift, and cloud-based data architectures.
5) Oil & Natural Gas Midstream domain experience (Mandatory) – Experience supporting pipeline, transportation, storage, LNG, natural gas, or other midstream operations and data environments
6) Claude AI / Generative AI experience (Mandatory) – Experience working with Claude, LLM-based solutions, AI-ready data platforms, RAG architectures, or Gen AI implementations.
7) No job hoppers Role Summary Required Skills
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.
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.