Data Engineer

Robotics Technologies is hiring a Data Engineer in Houston, TX (onsite) to design, build, and scale a modern cloud data platform centered on Snowflake and AWS. The role focuses on governed data pipelines and trusted data products, leveraging Iceberg managed tables, metadata services, and ontology-driven modeling.

Responsibilities

• 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 with dbt, including modular models, testing, documentation, and deployment workflows.
• Design and manage data structures in Snowflake as the central strategic data platform.
• Work with AWS-based data services and infrastructure to support 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 for 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.

Requirements

• Strong experience in data engineering, data modeling, and ETL/ELT, including cloud data platform development.
• Hands-on experience with Snowflake, including data modeling, performance optimization, access controls, and scalable warehouse or 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 and semantic modeling concepts, including 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.

Technologies

• Snowflake, AWS, Iceberg / Apache Iceberg
• Snowflake Catalog, Snowflake Horizon
• Informatica, dbt
• SQL, Python
• Open table formats
• Ontology-driven modeling, RDF, OWL, SHACL, SPARQL
• Graph databases, knowledge graph platforms
• CI/CD, Git
• Data observability, lineage tracking, data contracts, data quality frameworks
• Semantic layers, metadata models, domain ontologies

Preferred 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.