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Data Taxonomy Jobs in Arizona (NOW HIRING)

Lorven Technologies

Actimize Engineer

Phoenix, AZ · On-site

$84K - $106K/yr

Proficiency in SQL, PL/SQL, and scripting for data analysis and transformations. Experience ... Exposure to Fraud Taxonomy models, CRR, SAR workflows, multi channel ingestion. Understanding of ...

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Data Taxonomy information

What is the highest paid job in data science?

The highest paid roles in data science are often senior positions such as Lead Data Scientist, Data Science Director, or Chief Data Officer, which typically require extensive experience, advanced skills in machine learning and big data tools, and often involve strategic decision-making responsibilities. These roles can command salaries exceeding $150,000 annually, depending on the industry and location.

How to get a job in taxonomy?

To get a job in taxonomy, develop expertise in data organization, classification, and metadata standards such as Dublin Core or schema.org. Gaining skills in data management tools, understanding industry-specific vocabularies, and obtaining relevant certifications can improve employability. Experience with data analysis and information architecture is also valuable for roles in taxonomy development and management.

What are some typical challenges faced when developing and maintaining a data taxonomy within an organization?

One common challenge when working in data taxonomy is ensuring consistency across different departments that may use varied terminology or classification standards. Data taxonomists often need to facilitate collaboration between stakeholders to agree on definitions and structures, which requires strong communication and negotiation skills. Another challenge is keeping the taxonomy up-to-date as business needs and data sources evolve, necessitating regular reviews and updates. Successfully navigating these issues helps improve data discoverability, governance, and overall business intelligence.

What is data taxonomy?

Data taxonomy is a structured classification system that organizes data into categories and subcategories, making it easier to manage, search, and analyze. Data professionals often use standards and tools like metadata and ontologies to develop effective taxonomies for data governance and integration.

What is the difference between Data Taxonomy vs Data Analyst?

AspectData TaxonomyData Analyst
Primary FocusOrganizing and classifying data structuresAnalyzing data to extract insights
Skills & CertificationsData modeling, taxonomy development, data management certificationsStatistical analysis, SQL, data visualization skills
Work EnvironmentData management teams, data governance departmentsBusiness units, analytics teams
Industry UsageData governance, information architectureBusiness intelligence, reporting

Data Taxonomy involves creating structured classifications for data assets, ensuring consistency and clarity across systems. Data Analysts focus on interpreting data to support decision-making. While both roles work with data, Data Taxonomy emphasizes data organization, whereas Data Analysts analyze data for insights.

What do you need to be a data taxonomy specialist?

A data taxonomy specialist typically needs a strong understanding of data management, classification, and metadata standards, along with skills in data modeling and taxonomy development. Proficiency in tools like Excel, SQL, or specialized taxonomy software is often required, and relevant certifications in data management or information architecture can be beneficial. Experience with data governance and collaboration with cross-functional teams also supports success in this role.

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

To thrive as a Data Taxonomist, you need a strong background in information science, data organization, and metadata management, often supported by a degree in library science, information systems, or a related field. Familiarity with taxonomy management tools, data modeling software, and standards such as SKOS or RDF is commonly required. Attention to detail, analytical thinking, and effective communication are essential soft skills for collaborating across teams and ensuring data consistency. These skills and qualifications are crucial for creating structured data frameworks that improve data discoverability, usability, and governance.
What are popular job titles related to Data Taxonomy jobs in Arizona? For Data Taxonomy jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Data Taxonomy jobs in Arizona look for? The top searched job categories for Data Taxonomy jobs in Arizona are:
What cities in Arizona are hiring for Data Taxonomy jobs? Cities in Arizona with the most Data Taxonomy job openings:
Data Taxonomy jobs near you
Infographic showing various Data Taxonomy job openings in Arizona as of July 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Data Governance Analyst

Avani Technology Solutions, Inc.

Scottsdale, AZ • On-site

Full-time

Re-posted 4 days ago

Job description

Position : Data Governance Analyst
Location : Scottsdale, AZ
Duration : 18 Months
Top Three Skills:
1. Driving Data Governance for the program
2. Data Validation and Business Rules
3. Analysis/Data Quality
Job Description:
A data governance analyst performs a variety of tasks related to governance policies, process and standards. A data governance analyst will be responsible for data definitions, root cause analysis of data issues, reference data management, and mitigation of downstream impacts associated with a project/program. A data governance analyst will approve data exceptions and data quality metrics.
Required Experience/Skills
1. Experience with Insurance or Financial Services preferred
2. Working with business decision making models
3. Data centric with a passion toward data including, data quality issue resolution, defining business definitions, enforcement of data governance policies, process, and standards, and experience working with multiple partners to ensure data quality
4. Data Governance - Acted as a data governance professional, data steward, data custodian, or other data role
5. Data Analysis - Experience as data analyst in one of the following situations:
a. Analytically Intensive - Intended to confirm the common understanding of the data.
b. Operationally Data Intensive - Intended to approve authoritative data sources that are essential to core transactional processing
c. Data Integration Intensive - Intended to confirm the common understanding data being integrated and mitigate downstream impacts
1. Taxonomy - Term definitions, hierarchy and metadata
2. Communication - Facilitator, influencing, negotiation and building partnerships between teams or business units.
3. Experience with different applications to effectively present the findings whether it is through use of charts, graphs or specialized tools.
4. 5+ years in data governance analysis with experience listed above. Above plus demonstrated leadership, communication and influencing skills relative to program and organizational level efforts.

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