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Metadata Engineer Jobs (NOW HIRING)

System One

GenAI Context Engineer

Pittsburgh, PA · On-site

Define metadata, taxonomy, ontology, chunking, and embedding strategies to improve retrieval ... Collaborate with Value Engineers, architects, and business stakeholders to translate enterprise ...

E*Pro, Inc.

Informatica Metadata Manager

Charlotte, NC

$52 - $68.25/hr

Metadata Lineage * Design and implementation of a Metadata Repository. * Design and development of customizable interface for consumers of information within the Metadata Repository. * Support for ...

TMS

Data Architect - Active Metadata

Menlo Park, CA · Remote

$90 - $95/hr

Experience building closed-loop automation (e.g., metadata-triggered autonomous schema repair). - Semantic Engineering: Mastery of RDF, OWL, and SHACL for ontology-first modeling and SPARQL reasoning ...

i-Link Solutions

Data Architect - Active Metadata

$65.25 - $84/hr

Experience building closed-loop automation (e.g., metadata-triggered autonomous schema repair). • Semantic Engineering: Mastery of RDF, OWL, and SHACL for ontology-first modeling and SPARQL ...

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Metadata Engineer information

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$127.5K

$168.5K

How much do metadata engineer jobs pay per year?

As of Jul 23, 2026, the average yearly pay for metadata engineer in the United States is $166,304.00, according to ZipRecruiter salary data. Most workers in this role earn between $167,000.00 and $167,000.00 per year, depending on experience, location, and employer.

How does a Metadata Engineer typically collaborate with data governance and engineering teams?

Metadata Engineers play a crucial role in bridging the gap between data governance and engineering teams. They are responsible for designing and maintaining metadata repositories, ensuring data assets are well-documented and easily discoverable. On a daily basis, Metadata Engineers work closely with data engineers to implement metadata capture and lineage tracking, and they partner with governance teams to establish data quality and compliance standards. Effective collaboration is essential for maintaining data integrity and supporting organizational data strategies.

What are the key skills and qualifications needed to thrive as a Metadata Engineer, and why are they important?

To thrive as a Metadata Engineer, you need a strong background in data modeling, metadata standards, and database management, often supported by a degree in computer science or a related field. Familiarity with metadata management tools (such as Collibra or Alation), SQL, data catalog systems, and sometimes certifications in data governance are commonly required. Attention to detail, analytical thinking, and effective cross-team communication are crucial soft skills for this role. These skills ensure accurate metadata organization, support data discoverability, and enable effective data governance across an organization.

What is the difference between Metadata Engineer vs Data Engineer?

AspectMetadata EngineerData Engineer
Required CredentialsBachelor's in Computer Science, Information Systems, or related fields; certifications in data management or metadata standardsBachelor's in Computer Science, Software Engineering, or related fields; certifications in data engineering or cloud platforms
Work EnvironmentData management teams, data governance, data catalogingData pipelines, database architecture, big data platforms
Employer & Industry UsageTech companies, data-driven organizations, data management firmsTech companies, finance, healthcare, e-commerce, any industry handling large data sets

While both roles involve working with data, Metadata Engineers focus on managing data descriptions, standards, and catalogs to ensure data is discoverable and well-documented. Data Engineers build and maintain data pipelines and infrastructure to enable data analysis and reporting. Understanding these differences helps organizations assign the right roles for their data needs.

What are Metadata Engineers?

Metadata Engineers are specialized IT professionals who design, implement, and manage systems that capture, structure, and utilize metadata—data that describes other data—within an organization. Their work supports data governance, improves data discoverability, and enhances the consistency and quality of information across databases and platforms. Metadata Engineers often collaborate with data architects, data stewards, and business analysts to ensure that data assets are properly cataloged and easily accessible for analysis and compliance purposes.
More about Metadata Engineer jobs
What job categories do people searching Metadata Engineer jobs look for? The top searched job categories for Metadata Engineer jobs are:
Metadata Engineer jobs near you
Infographic showing various Metadata Engineer job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $166,304 per year, or $80 per hour.
GenAI Context Engineer

GenAI Context Engineer

System One

Pittsburgh, PA • On-site

Full-time

Posted 14 days ago

Job description

Job Title: GenAI Context Engineer Duration : Permanent Full Time Location : Strongsville, OH, Dallas, TX, or Pittsburgh, PA. Work Mode : 5 Days Onsite Looking to hire a Context Engineer who will be responsible for designing, building, and optimizing the enterprise knowledge and retrieval foundation that powers Generative AI applications. This role focuses on enabling high quality, grounded, and context aware AI experiences through Retrieval Augmented Generation (RAG), semantic search, metadata engineering, and enterprise knowledge orchestration. The Context Engineer ensures AI systems retrieve the right information, from the right sources, at the right time — securely, accurately, and in alignment with enterprise governance standards. Future duties and responsibilities

  • Design and implement enterprise Retrieval Augmented Generation (RAG) architectures for GenAI platforms and applications.
  • Build and optimize semantic retrieval pipelines, vector search implementations, and contextual grounding frameworks.
  • Develop ingestion pipelines for enterprise knowledge sources including SharePoint, Confluence, Jira, APIs, databases, and document repositories.
  • Define metadata, taxonomy, ontology, chunking, and embedding strategies to improve retrieval relevance and AI response quality.
  • Implement permission aware retrieval and secure knowledge access aligned with enterprise governance and compliance requirements.
  • Design and optimize hybrid search architectures combining vector search, keyword search, and knowledge graph capabilities.
  • Collaborate with Value Engineers, architects, and business stakeholders to translate enterprise knowledge into scalable AI ready knowledge structures.
  • Improve groundedness, citation accuracy, retrieval precision, and hallucination reduction across GenAI solutions.
  • Maintain knowledge lineage, auditability, and contextual traceability for enterprise AI workflows.
  • Support AI evaluation, observability, and continuous improvement initiatives for retrieval quality and search performance.
  • Work closely with governance, security, and compliance teams to ensure responsible and secure enterprise AI knowledge access.
  • Contribute to reusable enterprise knowledge engineering patterns and platform accelerators.
Required qualifications to be successful in this role
  • 6+ years of experience in knowledge engineering, enterprise search, data engineering, AI engineering, or platform engineering roles.
  • Experience building enterprise AI search or knowledge platforms in banking, financial services, healthcare, or other regulated industries.
  • Familiarity with knowledge graphs, ontology modeling, AI observability, and enterprise governance frameworks.
  • Understanding of responsible AI, groundedness evaluation, and enterprise compliance requirements for GenAI systems.
  • Hands on experience with Retrieval Augmented Generation (RAG), semantic search, embeddings, vector databases, and enterprise knowledge systems.
  • Strong programming skills in Python and experience with API based integrations.
  • Experience with GenAI and retrieval technologies such as: o Azure OpenAI / OpenAI o Azure AI Search o LangChain / Semantic Kernel o Elasticsearch / OpenSearch o Vector databases and embedding frameworks
  • Experience designing ingestion pipelines, metadata frameworks, chunking strategies, and contextual retrieval systems.
  • Strong understanding of enterprise data governance, access control, lineage, and permission aware retrieval. Experience integrating enterprise content systems including SharePoint, Confluence, Jira, document repositories, and enterprise APIs.
  • Familiarity with cloud platforms such as Azure, AWS, or GCP and containerized environments.
  • Strong analytical, troubleshooting, and problem solving skills.
  • Excellent communication and collaboration skills with the ability to work across engineering, architecture, governance, and business teams.
Required Skills:
  • LangChain
  • LangGraph
  • LlamaIndex
  • Microsoft Azure AI Solution
  • OpenAI
  • Python
  • Retrieval-Augmented Gen.(RAG)
Ref: #404-IT Pittsburgh