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Semantic Data Analyst Jobs (NOW HIRING)

Senior Data Analyst

Austin, TX · On-site

$85K - $107K/yr

... semantic models, reporting outputs, and downstream data solutions. • Research, diagnose, and ... analysis and continuous improvement efforts. Business Partnership & Solution Delivery • ...

Cloud Data Architect

Pittsburgh, PA · Remote

$65.25 - $84/hr

Define semantic data models that enable self-service analytics and consistent reporting, in coordination with data governance, metadata management, and master data management (MDM) standards.

Cloud Data Architect

Pittsburgh, PA · On-site +1

$62 - $79.50/hr

Define semantic data models that enable self-service analytics and consistent reporting, in coordination with data governance, metadata management, and master data management (MDM) standards.

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Semantic Data Analyst information

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$34K

$82.6K

$136K

How much do semantic data analyst jobs pay per year?

As of Sep 11, 2026, the average yearly pay for semantic data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What is a semantic data analyst?

A Semantic Data Analyst is a professional who specializes in interpreting, organizing, and analyzing data using semantic technologies. They work with ontologies, taxonomies, and metadata to add meaning and context to data, making it more useful for complex analysis and machine learning applications. Semantic Data Analysts help organizations structure their data, improve searchability, and enable smarter data integration by focusing on the relationships and meanings within data sets. Their work often involves using tools and languages such as RDF, OWL, and SPARQL to manage and query semantic data.

What are the key skills and qualifications needed to thrive as a semantic data analyst?

To thrive as a Semantic Data Analyst, you need a strong background in data analysis, knowledge representation, and experience with ontologies, often supported by a degree in computer science or information science. Proficiency with semantic web technologies such as RDF, OWL, SPARQL, and familiarity with data integration tools and graph databases is typically required. Strong analytical thinking, attention to detail, and effective communication skills help you interpret complex data relationships and explain insights to stakeholders. These skills are crucial for extracting meaningful insights from structured data, enabling effective decision-making and knowledge management within organizations.

How does a semantic data analyst typically collaborate with other teams within an organization?

Semantic Data Analysts often work closely with data engineers, software developers, and subject matter experts to ensure that data is accurately structured, labeled, and interpreted according to organizational needs. They play a key role in translating business requirements into semantic models or ontologies, which helps improve data interoperability and accessibility. Regular meetings with cross-functional teams are common, as analysts provide guidance on metadata standards and support the integration of semantic technologies into various projects.

What is the difference between Semantic Data Analyst vs Data Analyst?

AspectSemantic Data AnalystData Analyst
Required CredentialsBachelor's in Data Science, Computer Science, or related field; knowledge of semantic web technologiesBachelor's in Data Analysis, Statistics, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentData-driven projects, semantic web platforms, knowledge graphsBusiness intelligence, reporting, data cleaning, and visualization
Industry UsageTech, research, organizations utilizing semantic web and linked dataFinance, marketing, healthcare, and general business sectors

The Semantic Data Analyst specializes in semantic web technologies and linked data, focusing on structuring and interpreting data within knowledge graphs. In contrast, the Data Analyst primarily handles data collection, cleaning, and visualization for business insights. Both roles require strong analytical skills, but the Semantic Data Analyst's expertise in semantic technologies distinguishes their focus on web semantics and knowledge representation.

What are popular job titles related to Semantic Data Analyst jobs?

For Semantic Data Analyst jobs, the most frequently searched job titles are:

Infographic showing various Semantic Data Analyst job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $82,640 per year, or $39.7 per hour.

Principal Software Engineer, Semantic Data Services

San Francisco, CA • On-site

$159K - $213K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 21 days ago


Key responsibilities

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

  • Design and build semantic business objects that integrate enterprise data, relationships, business context, and governance.

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


Job description

Principal Software Engineer, Semantic Data Services

About the Role

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.

How you'll make an impact
  • 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.

Experience you'll bring
  • 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.

Preferred experience
  • 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.

The base pay range represents the anticipated low and high end of the pay range for this position. Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role. Base pay is one component of WEX's total compensation package. Most sales positions are eligible for commission under the terms of an applicable plan. Non-sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan. WEX's comprehensive and market competitive benefits are designed to support your personal and professional well-being. Benefits include health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more. For more information, check out the "About Us" section.Pay Range: $200,600.00 - $250,400.00