Data Engineer – Cloud Data Platform

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

Role Summary

We are seeking 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 building enterprise data platforms, and the ability to work with ontologies, semantic models, metadata, and governed data products.

This role supports strategic data initiatives using Snowflake, AWS, Apache Iceberg managed tables, Snowflake Catalog, Snowflake Horizon, Informatica, and dbt. The successful candidate will help create trusted, reusable data assets that enable applications, analytics, AI, and business intelligence solutions.

Key Responsibilities

• Design, build, and maintain scalable data pipelines for structured, semi-structured, and unstructured data.

• Develop data ingestion and extract-load (EL) processes using Informatica or comparable enterprise data integration tools.

• Build transformation logic using dbt, including modular models, testing, documentation, and deployment workflows.

• Design, optimize, and manage data structures within Snowflake as the organization's strategic cloud data platform.

• Develop and support data solutions within AWS cloud environments.

• Implement data architectures using Apache Iceberg managed tables, leveraging open table formats, interoperability, cataloging, and governed access patterns.

• Utilize Snowflake Catalog and Snowflake Horizon to support metadata management, data discovery, lineage, governance, policy enforcement, and trusted data sharing.

• Collaborate with data architects, governance teams, analysts, application developers, and business stakeholders to design trusted data products.

• Partner with business and domain experts to define business concepts, entities, relationships, and terminology.

• Support ontology-driven data modeling, including semantic models, taxonomies, business glossaries, and metadata management.

• Translate business requirements into logical and physical data models and reusable enterprise data products.

• Implement data quality validation, lineage, observability, governance controls, and monitoring.

• Support data products used for analytics, artificial intelligence, business intelligence, and operational applications.

• Ensure data solutions meet enterprise requirements for security, privacy, access control, scalability, performance, and reliability.

Required Qualifications

• Strong experience in data engineering, data modeling, ETL/ELT development, and cloud-based data platforms.

• Hands-on experience with Snowflake, including data modeling, performance optimization, security, access controls, and scalable warehouse/lakehouse architectures.

• Experience working in AWS cloud environments.

• Experience with Informatica or similar enterprise data integration platforms.

• Experience with dbt for data transformations, testing, documentation, and analytics engineering workflows.

• Understanding of Apache Iceberg or similar open table formats, including managed tables, schema evolution, interoperability, and catalog-based access.

• Familiarity with data cataloging, governance, metadata management, lineage, and policy-based data access.

• Understanding of ontology modeling, semantic data models, taxonomies, business glossaries, or knowledge graph concepts.

• Strong SQL skills and proficiency in Python or another modern programming language used for data engineering.

• Ability to collaborate with business stakeholders to define data entities, relationships, metrics, and data product requirements.

• Excellent communication, documentation, analytical, and problem-solving skills.

Preferred Qualifications

• Experience with Snowflake Catalog and Snowflake Horizon.

• Experience building governed data products supporting analytics, AI, machine learning, or operational applications.

• Experience with enterprise data governance platforms, particularly Informatica governance capabilities.

• Knowledge of RDF, OWL, SHACL, SPARQL, graph databases, or knowledge graph technologies.

• Experience designing semantic layers, metadata models, business glossaries, or domain ontologies.

• Familiarity with Git, CI/CD pipelines, automated testing, and deployment practices for data engineering.

• Experience implementing data observability, lineage tracking, data contracts, and data quality frameworks.

• Experience working within large-scale enterprise data environments supporting multiple business domains and stakeholder groups.

Ideal Candidate Profile

The ideal candidate is a hands-on Data Engineer who enjoys building scalable, reliable data pipelines while ensuring enterprise data is trusted, governed, discoverable, and meaningful. They have experience working across modern cloud data platforms and understand how technical implementation, business semantics, governance, and metadata work together to create reusable, high-quality data products.

Success in this role requires balancing strong technical engineering skills with an appreciation for enterprise data architecture, governance, semantic modeling, and the delivery of business-ready data solutions.

Must-Have Requirements
1) Strong Snowflake Data Architecture experience – Data modeling, data warehouse/lakehouse 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 GenAI implementations.