Lead Data Engineer – Snowflake Architecture

Umanist Staffing•Houston, TX

5h•Onsite

About The Position

We are seeking an experienced Senior Data Engineer with deep expertise in Snowflake, AWS, Data Governance, and enterprise-scale data platforms. The ideal candidate will have experience building trusted, governed, and scalable data products while supporting business intelligence, analytics, AI, and operational use cases. This role requires strong hands-on experience with Snowflake architecture, metadata management, ontology-driven data modeling, AWS cloud services, and modern data engineering practices. Experience within the Oil & Natural Gas Midstream industry and exposure to Claude AI or Generative AI solutions are mandatory.

Requirements

8+ years of experience in Data Engineering, Data Architecture, or Enterprise Data Platform development.
Strong hands-on experience with Snowflake Data Architecture, including: Data Modeling, Data Warehouse Design, Lakehouse Architecture, Performance Optimization, Security & Access Controls, Enterprise-Scale Implementations.
Strong experience with AWS cloud services including: S3, Glue, Lambda, Athena, EMR, Redshift.
Experience with Informatica or similar enterprise data integration tools.
Experience with dbt for data transformations, testing, and analytics engineering.
Strong SQL and Python development skills.
Expertise in Data Governance, including: Data Cataloging, Metadata Management, Data Lineage, Data Quality, Governance Frameworks, Policy-Based Data Access.
Understanding of Ontology, Semantic Modeling, Taxonomies, Business Glossaries, and Knowledge Graph concepts.
Experience working with Apache Iceberg or open table formats.
Strong communication and stakeholder management skills.
Oil & Natural Gas Midstream Experience (Required): Candidates must have direct experience supporting: Pipeline Operations, Natural Gas Transportation, LNG Operations, Storage & Distribution, Midstream Data Platforms, Energy Operations & Analytics.
Claude AI / Generative AI Experience (Required): Candidates must have experience with one or more of the following: Claude AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI-Ready Data Platforms, Enterprise GenAI Implementations, Semantic Search Solutions.
Demonstrated job stability with consistent tenure. Frequent job hoppers will not be considered.

Nice To Haves

Experience with Snowflake Catalog and Snowflake Horizon.
Experience building governed enterprise data products.
Experience with enterprise metadata and governance platforms.
Experience with RDF, OWL, SHACL, SPARQL, Graph Databases, or Knowledge Graph platforms.
Experience with CI/CD, Git, automated testing, and deployment pipelines.
Experience with data observability, data contracts, and advanced data quality frameworks.
Experience working within complex enterprise environments supporting multiple business domains.

Responsibilities

Design, build, and maintain scalable data pipelines across structured, semi-structured, and unstructured data sources.
Develop and manage enterprise data solutions using Snowflake as the strategic data platform.
Build and maintain ELT/ETL processes using Informatica and modern data integration frameworks.
Develop transformation models, testing frameworks, and documentation using dbt.
Design and manage Snowflake data warehouses, lakehouses, schemas, and access controls.
Implement and support Apache Iceberg managed tables and open table architectures.
Support metadata management, lineage tracking, governance, cataloging, and policy-driven access using Snowflake Catalog and Snowflake Horizon.
Collaborate with business stakeholders to define data entities, relationships, metrics, and reusable data products.
Support ontology-driven data modeling, semantic layers, taxonomies, business glossaries, and knowledge management initiatives.
Implement data quality controls, observability frameworks, lineage tracking, and governance standards.
Support AI-ready data architectures, RAG solutions, and Generative AI initiatives.
Ensure data security, privacy, reliability, and compliance across all data assets.