Principal Software Engineer, Semantic Data Services

Principal Software Engineer, Semantic Data Services

WEX is reimagining its enterprise data platform with an ambitious goal: make our data understandable not only to people and applications, but also to AI models and autonomous agents.

We are looking for a Principal Software Engineer to help build the next generation of our Data-as-a-Service (DaaS) platform and the semantic foundation that connects enterprise data with its business meaning.

In this role, you will help define and build trusted semantic objects for our major business domains-such as customers, accounts, merchants, transactions, vehicles, payments, claims, and risk signals. These objects will bring together data, relationships, business definitions, derived attributes, policies, lineage, and context into reusable assets that can power analytics, data products, decisioning, and AI experiences.

You will operate as a senior technical leader and hands-on engineer, partnering with engineers, architects, Product, AI, Analytics, Risk, Finance, and business teams across WEX to solve complex data and distributed systems problems.

The space is evolving quickly. We are not looking for someone who has already built an exact version of this platform. We are looking for an engineer with deep data and platform expertise, strong technical judgment, and the ability to help invent what the next generation of enterprise data platforms should become in an AI-native world.

• Define and influence the architecture and technical direction for WEX's Semantic Data Lake and next generation of Data as a Service.

• Design and build semantic business objects that bring together enterprise data, business context, relationships, metrics, derived attributes, rules, lineage, quality, and governance.

• Develop scalable patterns and frameworks for creating semantic objects across multiple lines of business while maintaining consistent enterprise standards.

• Build core platform capabilities across semantic modeling, ontology, metadata, knowledge graphs, data quality, lineage, data contracts, and governance.

• Work deeply with business and product teams to translate complex business concepts into durable technical models and reusable platform capabilities.

• Help capture business context that may exist across databases, applications, workflows, documents, policies, and institutional knowledge.

• Enable AI and agentic applications to discover, understand, reason over, and safely act on trusted enterprise data.

• Design reusable APIs, services, frameworks, and developer experiences that allow teams to build data products and AI experiences faster.

• Solve complex distributed systems, data processing, scalability, reliability, and performance challenges.

• Establish engineering patterns and architectural standards and influence adoption across teams without relying on direct authority.

• Mentor engineers and provide technical guidance on complex architecture and implementation decisions.

• Remain hands-on with architecture, design, prototyping, code, and critical platform components.

• 10+ years of experience in software engineering, data engineering, distributed systems, data platforms, or related technology areas, with demonstrated experience operating at Staff, Principal, or equivalent technical scope.

• Deep software engineering and distributed systems expertise, with experience designing and building large-scale production platforms.

• Deep understanding of modern data architecture, including large-scale data processing, streaming, lakehouse architectures, data products, and distributed systems.

• Deep understanding of semantic data technologies and industry direction is essential. Direct experience building a semantic data layer is strongly recommended.

• Experience with semantic modeling, ontology, metadata platforms, knowledge graphs, data catalogs, enterprise data modeling, or closely related technologies.

• Demonstrated ability to translate complex business concepts into scalable technical abstractions and reusable platform capabilities.

• Strong understanding of data quality, governance, lineage, security, privacy, and operational reliability.

• Experience building platforms or capabilities adopted by multiple engineering teams, business domains, or product organizations.

• Strong architectural judgment and the ability to reason through complex tradeoffs while moving comfortably between architecture and detailed implementation.

• Demonstrated ability to influence technical direction and drive alignment across teams and organizational boundaries.

• Strong communication skills with the ability to explain complex technical concepts to engineers, product teams, business partners, and senior leaders.

• Experience building platforms that support AI/ML, generative AI, RAG, or agentic applications.

• Experience with knowledge representation, semantic search, context engineering, graph technologies, or AI-ready enterprise data.

• Experience modernizing large enterprise data ecosystems with multiple business domains and legacy systems.

• Experience with technologies such as Spark, Flink, Kafka, Iceberg, Snowflake, Databricks, cloud-native data services, graph databases, or equivalent large-scale data technologies.

• Experience defining reusable frameworks, APIs, standards, or platform capabilities that have been broadly adopted beyond an individual team.