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Data Analyst Jobs in Rio Rancho, NM (NOW HIRING)

We are hiring a Principal Analyst Data Manager to work onsite in Tucson, Arizona. What You Will Do * Identifies all data deliverables made part of a contract * Builds a data track in a Product Data ...

As a CACI Mission & Engineering NAVWAR Analyst (RF Data Analyst) supporting the Joint Navigation Warfare Center (JNWC), you will contribute to enhancing the Department of Defense's (DoD ...

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Data Analyst information

See Rio Rancho, NM salary details

$30.7K

$74.5K

$122.7K

How much do data analyst jobs pay per year?

As of Sep 9, 2026, the average yearly pay for data analyst in Rio Rancho, NM is $74,533.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,400.00 and $87,500.00 per year, depending on experience, location, and employer.

What does a data analyst do?

A Data Analyst is responsible for collecting, processing, and analyzing data to help organizations make informed business decisions. They use statistical tools and software to interpret data sets, identify trends, and create visual reports. Data Analysts often collaborate with other departments to provide actionable insights and support strategic planning. Their work helps organizations optimize operations, track performance, and solve business problems using data-driven approaches.

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

To thrive as a Data Analyst, you need strong analytical skills, proficiency in statistics, and a relevant degree such as in mathematics, statistics, or computer science. Familiarity with data analysis tools like SQL, Excel, Python or R, and experience with visualization platforms such as Tableau or Power BI are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help analysts interpret data insights and present findings clearly to stakeholders. These skills are crucial for transforming raw data into actionable business insights that drive informed decision-making.

What are some common challenges data analysts face when working with large datasets, and how are they typically addressed?

Data Analysts often encounter challenges such as data quality issues, missing or inconsistent values, and slow processing times when handling large datasets. These challenges are typically addressed by implementing data cleaning routines, using advanced data management tools, and leveraging programming languages like Python or R for efficient data manipulation. Collaboration with database administrators and IT teams is also common to ensure data integrity and optimize data storage solutions. Staying updated with best practices in data wrangling and visualization helps Data Analysts deliver accurate and actionable insights.

What is the difference between Data Analyst vs Data Scientist?

AspectData AnalystData Scientist
Required CredentialsBachelor's degree in statistics, mathematics, or related field; often certifications in data analysis toolsBachelor's or master's in computer science, statistics, or related; often advanced certifications or degrees
Work EnvironmentBusiness settings, focusing on data reporting and visualizationResearch and development environments, focusing on predictive modeling and complex algorithms
Employer & Industry UsageRetail, finance, healthcare, and marketing companiesTech firms, research institutions, and large enterprises

While both roles analyze data, Data Analysts primarily focus on interpreting existing data to generate reports and insights, whereas Data Scientists develop predictive models and advanced algorithms to forecast trends and solve complex problems.

Do data analysts get paid well?

Data analysts typically earn competitive salaries that vary by experience, location, and industry. Entry-level positions may start lower, but with skills in tools like Excel, SQL, and data visualization, salaries tend to increase with expertise and certifications. Overall, data analysis is considered a well-paying field with growth potential.

Is it hard to get a data analyst job?

Securing a data analyst position can be competitive, as it often requires strong skills in data manipulation, statistical analysis, and proficiency with tools like Excel, SQL, or Python. Candidates with relevant education, certifications, and experience tend to have better chances, but persistence and continuous skill development are important.

What work does a data analyst do?

A data analyst collects, processes, and analyzes large datasets to identify trends, patterns, and insights that support business decision-making. They use tools like Excel, SQL, and data visualization software to interpret data and communicate findings to stakeholders. Strong analytical skills and attention to detail are essential for this role.

What are the most commonly searched types of Data Analyst jobs in Rio Rancho, NM?

The most popular types of Data Analyst jobs in Rio Rancho, NM are:

What are popular job titles related to Data Analyst jobs in Rio Rancho, NM?

For Data Analyst jobs in Rio Rancho, NM, the most frequently searched job titles are:

What job categories do people searching Data Analyst jobs in Rio Rancho, NM look for?

The top searched job categories for Data Analyst jobs in Rio Rancho, NM are:

What cities near Rio Rancho, NM are hiring for Data Analyst jobs?

Cities near Rio Rancho, NM with the most Data Analyst job openings:

Infographic showing various Data Analyst job openings in Rio Rancho, NM 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 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $74,533 per year, or $35.8 per hour.

Principal Data Analyst - Albuquerque, NM (Hybrid)

Albuquerque, NM • On-site

RS21
IT Services • 51 - 200 employees

$125 - $150/hr

Other

Posted 11 days ago


Key responsibilities

  • Lead the design and execution of complex analytical projects, including data integration, longitudinal analysis, geospatial analysis, and statistical modeling.

  • Translate high-stakes, ambiguous business questions into rigorous analytical frameworks and synthesize findings into executive-level narratives and visualizations.

  • Define and promote analytical standards, evaluate tools and technologies, and serve as the technical authority on analytical questions.


Job description

If you are unable to complete this application due to a disability, contact this employer to ask for an accommodation or an alternative application process.

Full Time Albuquerque, NM, US

Salary Range: $118,000.00 To $158,000.00 Annually

Position Summary

RS21 is seeking a Principal Data Analyst to serve as the organization's foremost authority on data analysis, analytical methods, and the translation of complex data into actionable intelligence for mission-driven clients. This is the highest individual contributor level within the Data Analytics job family, and it carries significant responsibility for shaping how RS21 approaches analysis, insight delivery, and data-informed decision-making across its entire portfolio. This is a hybrid position based out of our Albuquerque, NM office.

The Principal Data Analyst leads the most complex and strategically significant analytical engagements, advises clients at the executive level, and sets the analytical standards and practices that define RS21's quality and differentiation in the market. Beyond delivering exceptional analytical work, this role is expected to grow the capabilities of the broader analytics team through mentorship, methodology development, and thought leadership.

The ideal candidate is a seasoned analytical practitioner who combines deep technical expertise with executive presence, intellectual curiosity, and a genuine passion for using data to solve hard problems in healthcare, government, and public-interest domains.

Key Responsibilities Advanced Analytics & Insight Delivery
  • Lead the design and execution of the most complex analytical projects, including multi-source data integration, longitudinal analysis, geospatial analysis, and statistical modeling in support of client decision-making.
  • Translate ambiguous, high-stakes business questions into rigorous analytical frameworks, ensuring methodological soundness and interpretive clarity.
  • Apply advanced statistical techniques including regression modeling, time-series analysis, clustering, hypothesis testing, and causal inference to produce defensible, high-impact findings.
  • Develop and maintain sophisticated analytical pipelines, dashboards, and reporting systems that deliver ongoing intelligence to clients and internal stakeholders.
  • Validate and quality-assure analytical outputs produced by junior and senior analysts, ensuring accuracy, reproducibility, and appropriate handling of data limitations.
  • Synthesize findings from large, complex datasets into executive-level narratives, visualizations, and recommendations that drive client action.
Technical Leadership & Standards
  • Define and champion RS21's analytical standards, including best practices for data validation, reproducible analysis, visualization design, and documentation.
  • Evaluate, recommend, and guide adoption of analytical tools, platforms, and technologies that improve team capability and delivery quality.
  • Establish code and methodology review processes that ensure consistency and excellence across the Data Analytics practice.
  • Lead the development of reusable analytical frameworks, templates, and accelerators that increase team velocity on common problem types.
  • Serve as the technical authority on analytical questions that require senior judgment, including data quality trade-offs, methodological choices, and interpretive risk.
  • Stay at the forefront of developments in data analytics, visualization, and applied statistics, bringing relevant innovations into RS21's practice.
Client & Stakeholder Engagement
  • Serve as the senior analytical relationship owner for RS21's most strategically important client accounts, building trust as a long-term advisory partner.
  • Engage directly with executive and senior client stakeholders to understand their data maturity, analytical needs, and decision-making context.
  • Lead client-facing workshops, analytical briefings, and data strategy sessions that deepen client engagement and expand the scope of analytical partnership.
  • Communicate complex analytical findings with clarity and confidence to non-technical audiences, including agency heads, program executives, and policy makers.
  • Anticipate evolving client analytical needs and proactively propose analytical approaches, capability investments, or scope expansions that deliver additional value.
  • Mentor and develop analysts at all levels within the Data Analytics practice, serving as a primary technical role model and professional development resource.
  • Lead communities of practice, peer learning sessions, and internal knowledge-sharing initiatives that elevate the team's collective analytical capability.
  • Partner with talent and HR leadership to define competency frameworks, career pathways, and hiring criteria for the Data Analytics job family.
  • Contribute to onboarding programs for new analysts, ensuring they are equipped with RS21's tools, standards, and client engagement expectations from day one.
  • Identify skill gaps across the analytics team and recommend targeted learning investments, training programs, or hiring strategies to address them.
Business Development & Organizational Strategy
  • Serve as the analytical subject matter expert on high-value and strategic proposals, shaping technical approach, staffing rationale, and differentiated win themes.
  • Contribute to capture strategy, client discovery, and competitive positioning for analytics-focused opportunities across RS21's priority markets.
  • Advise executive leadership on analytical trends, emerging client needs, and market opportunities that should inform RS21's growth strategy.
  • Represent RS21's analytical capabilities at industry conferences, client forums, and external venues through presentations, publications, or panel participation.
  • Build relationships with academic institutions, technology partners, and peer organizations that strengthen RS21's analytical ecosystem and talent pipeline.
Qualifications Required
  • Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Public Policy, or a related quantitative field. Advanced degree (Master's or PhD) strongly preferred.
  • 10+ years of progressive experience in data analysis, business intelligence, or applied analytics, including at least 3 years in a senior or lead analyst role.
  • Expert-level proficiency in SQL and at least one analytical programming language such as Python or R, including experience with libraries for data manipulation, statistical analysis, and visualization.
  • Deep experience designing and delivering complex analytical projects from problem definition through insight delivery, including work with messy, incomplete, or multi-source datasets.
  • Advanced proficiency with data visualization and business intelligence platforms such as Tableau, Power BI, Looker, or equivalent.
  • Strong grounding in applied statistics, including regression, significance testing, sampling methodology, and uncertainty quantification.
  • Proven track record of engaging and influencing executive-level stakeholders through data-driven analysis and storytelling.
  • Demonstrated experience mentoring analysts and contributing to team capability development.
Preferred
  • Advanced degree in a quantitative discipline such as Statistics, Applied Mathematics, Data Science, Public Health, or related field.
  • Experience working with federal, state, or local government clients, including familiarity with government data standards, privacy regulations (e.g., HIPAA, FERPA), and reporting requirements.
  • Background in healthcare analytics, population health, program evaluation, or social services data.
  • Experience with geospatial analysis tools such as ArcGIS, QGIS, or Python-based spatial libraries.
  • Familiarity with cloud-based data platforms such as AWS Redshift, Google BigQuery, Snowflake, or Azure Synapse.
  • Experience with longitudinal data, panel data methods, or quasi-experimental evaluation designs.
  • Published work, conference presentations, or other recognized contributions to the data analytics field.
  • Experience contributing to or leading data strategy, analytics roadmap, or data governance initiatives at an organizational level.
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