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Metadata Engineer Jobs (NOW HIRING)

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 reasoning ...

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$168.5K

How much do metadata engineer jobs pay per year?

As of Jun 26, 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 much do Meta engineers get paid?

Meta engineers typically earn a base salary ranging from $100,000 to $180,000 annually, depending on experience, location, and role level. Total compensation often includes bonuses, stock options, and benefits, which can significantly increase overall earnings.

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 engineers make $500,000?

Senior engineers in specialized fields such as software engineering, data engineering, or machine learning engineering can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries. Compensation often includes base salary, bonuses, and stock options, particularly at large tech companies or startups with significant growth potential.

Is AI replacing data engineers?

AI is automating certain tasks within data engineering, such as data processing and pipeline management, but it does not replace the need for data engineers. Data engineers are essential for designing, building, and maintaining data infrastructure, and their expertise in tools like SQL, Python, and cloud platforms remains critical in managing complex data systems.

What engineers make $300,000 a year?

Senior-level engineers in specialized fields such as software engineering, data engineering, and machine learning engineering can earn $300,000 or more annually, especially with extensive experience, advanced skills, and working in high-demand industries or companies. These roles often require expertise in programming, cloud platforms, and relevant certifications, and may include bonuses and stock options.

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
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Metadata Engineer jobs near you
Infographic showing various Metadata Engineer job openings in the United States as of June 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $166,304 per year, or $80 per hour.
Data Architect - Active Metadata

Data Architect - Active Metadata

Software Technology Inc

Remote

$65.25 - $84/hr

Other

Posted 3 days ago

Job description

Data Architect

Location: Menlo Park, CA (Remote)

Note: Education M.S./Ph.D. in Computer Science (Formal Methods/Logic) or Computational Mathematics.

Role Overview: We are seeking a visionary to architect a Self-Healing, Autonomous Data Fabric. You will replace legacy ETL with a "nervous system" where metadata is active, governance is computational, and data sharing is zero-copy.

Mandatory Skills:

  • Active Metadata: 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.
  • Production-level Open Policy Agent (OPA)/ Policy-as-Code (Zero-Trust) for dynamic, context-aware access control.

Other Technical Skills:

  • Advanced Privacy: Implementation of Homomorphic Encryption (FHE) or SMPC for analytics on encrypted PII.
  • Zero-Copy Architecture: Expertise in Delta Sharing for cross-cloud analytics without egress.
  • Compute: Trino (GraalVM), StarRocks, DuckDB (WASM).
  • Orchestration: Dagster, Airflow (Provider-level).
  • Semantic Layer: Stardog, Apache Jena, GraphQL Federation.
  • System Languages: Rust, Clojure, or Java.

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