Senior Semantic Data Engineer

Prologis•Nashville, TN

1d•$104,000 - $144,000

About The Position

Prologis is transforming how work gets done by bringing trusted data, automation, and AI into the core of its operations. As a Senior Semantic Data Engineer on the Data Solutions team, you will help create a shared enterprise understanding of business concepts, relationships, rules, and measures so people, applications, processes, and AI can interpret and use data consistently. This multidisciplinary role combines senior data engineering, business analysis, semantic modeling, AI evaluation, and enablement, with close partnership across business leaders, domain experts, and technical teams. You will carry solutions from discovery through production, helping Prologis improve decision confidence, accelerate solution delivery, and scale automation and AI without recreating business logic for each use case.

Requirements

Five or more years of experience in data engineering, data modeling, analytics engineering, or a related discipline.
Experience designing conceptual, logical, physical, and dimensional data models and translating business concepts into production data products, including grain, relationships, metrics, identity, and historical change.
Advanced SQL skills.
Experience designing and delivering production data products in Snowflake.
Experience with semantic layers, reusable metric definitions, and business metadata that support natural-language data access, enterprise search, copilots, or AI agents.
Experience managing technical assets through source control, code review, automated testing, deployment, and change-management practices.
Practical experience using approved AI tools in analysis, engineering, documentation, testing, or problem-solving, with disciplined validation of AI-generated outputs.
Communication and facilitation skills demonstrated through work with business stakeholders, translation between business and technical teams, ambiguity resolution, influence without direct authority, and leadership across collaborating teams.

Nice To Haves

Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, or a related field, or equivalent practical experience.
Experience with Snowflake Cortex semantic views, Cortex Analyst, Cortex Agents, or comparable AI-enabled semantic-layer technologies.
Experience creating evaluation datasets, verified query libraries, or validation frameworks for analytics, natural-language-to-SQL, or AI solutions.
Experience with enterprise data catalogs, metadata management, lineage, and data-quality platforms.
Experience using Python or a similar programming language and machine-readable formats such as YAML to create and maintain data and metadata assets, including exposure to APIs or MCP-based integrations; exposure to controlled vocabularies, taxonomies, knowledge graphs, or ontologies is also useful.
Demonstrates willingness and capability to leverage emerging technology, automation, and AI tools to improve efficiency, quality, and speed. Exercises sound judgment, creative thinking, and accountability for outcomes.

Responsibilities

Design and deliver trusted, reusable Snowflake data products for business domains, translating source data into certified dimensional structures, reusable views, and data contracts while resolving identity and modeling historical change.
Build and maintain the semantic layer by defining shared business concepts, terminology, relationships, rules, and measures that support consistent use of enterprise data across analytics, applications, business processes, automation, and AI.
Implement semantic data products using Snowflake Cortex semantic views and analytical Cortex Agents, applying approved AI capabilities to improve delivery speed, quality, and consistency while remaining accountable for outputs.
Validate semantic data products for business accuracy, data quality, usability, performance, security, and production readiness.
Develop and maintain representative business questions, verified queries, expected results, and regression tests; investigate inaccurate or inconsistent AI-generated answers and improve the underlying data, semantic model, instructions, or examples.
Capture definitions, mappings, ownership, lineage, and user guidance in version-controlled, machine-readable formats and publish this content to the enterprise data catalog through automated processes.
Lead complex semantic-modeling initiatives from business and source discovery through technical design, implementation, production deployment, and continuous improvement, partnering with business owners and domain experts across business functions, processes, systems, and regional variations.