Senior Data Analyst

Corporate Solutions Tech; Albuquerque (NM); On-site; USD 110,000 - 160,000; Full time

Job summary

Corporate Solutions Tech in Albuquerque, NM seeks a senior data analytics leader to drive complex projects from problem definition to delivery. You will build robust analytical frameworks, integrate multi-source data, and apply geospatial techniques to produce actionable insights.

Working with senior stakeholders, you’ll translate business questions into rigorous analyses, guide junior analysts, and present results with clarity.

Qualifications

• Bachelor's degree in a quantitative field; Master's degree preferred.
• 6–10 years of progressive experience in data analysis, BI, or applied analytics with leadership.
• Expert-level proficiency in SQL and Python or R, incl. libraries for data manipulation, stats, and visualization.
• Advanced proficiency with BI platforms: Tableau, Power BI, Looker, or equivalents.
• Strong grounding in applied statistics: regression, significance testing, sampling, and uncertainty.
• Experience presenting analytical findings to senior stakeholders and translating outputs into clear narratives.
• Experience working with messy, multi-source datasets in government, healthcare, or regulated industries.
• Mentoring analysts and contributing to team quality and capability development.

Responsibilities

• Lead the design and execution of complex analytical projects, including multi-source data integration and geospatial analysis.
• Translate ambiguous questions into rigorous analytical frameworks with defensible findings.
• Apply advanced statistics to produce reproducible outputs.
• Develop dashboards, pipelines, and reporting systems for ongoing client and internal insights.
• Synthesize findings into executive narratives and actionable recommendations.

Skills

SQL

Python

R

Statistics

Data storytelling

Stakeholder communication

Education

Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Public Policy, Public Health, or related quantitative field

Master's degree preferred

Tools

Tableau

Power BI

Looker

ArcGIS

Job description

Key Responsibilities

• Lead the design and execution of complex analytical projects from problem definition through delivery, including multi-source data integration, longitudinal analysis, statistical modeling, and geospatial analysis.
• Translate ambiguous, high-stakes business and policy questions into rigorous analytical frameworks, ensuring methodological soundness, interpretive clarity, and defensible findings.
• Apply advanced statistical techniques including regression modeling, hypothesis testing, time-series analysis, clustering, and cohort analysis to produce high-quality, reproducible analytical outputs.
• Develop and maintain sophisticated dashboards, analytical pipelines, and reporting systems that deliver ongoing intelligence to clients and internal stakeholders.
• Synthesize findings from complex, large-scale datasets into clear executive narratives, visualizations, and actionable recommendations tailored to technical and non-technical audiences.
• Validate and quality-assure analytical work produced by junior analysts, ensuring accuracy, consistency, and appropriate handling of data quality issues and analytical limitations.

Client Engagement & Stakeholder Communication

• Serve as a senior analytical point of contact for assigned client engagements, building trusted relationships with program managers, data teams, and senior client stakeholders.
• Lead client-facing analytical briefings, data review sessions, and findings presentations, communicating complex results with confidence and clarity.
• Translate evolving client needs and feedback into analytical adjustments, scope refinements, or new workstreams in coordination with project managers and technical leads.
• Support account managers and project managers in identifying analytical expansion opportunities within existing client accounts by staying attuned to unmet client data needs.
• Contribute to client onboarding and data discovery processes, helping clients understand their data landscape and how our analytical capabilities can address their most pressing challenges.

Individual Technical Contribution

• Serve as an individual contributor on complex and high-priority analytical deliverables, applying senior-level expertise directly to client work rather than delegating core analytical tasks.
• Write clean, well-documented, and reproducible analytical code in Python, R, or SQL, contributing directly to analytical pipelines, exploratory analysis, and production-ready reporting.
• Take ownership of end-to-end analytical workstreams including data extraction, cleaning, transformation, analysis, and visualization, ensuring high standards at every stage.
• Stay current with evolving tools, statistical methods, and visualization best practices within the data analytics field, bringing relevant innovations into our day-to-day analytical practice.
• Apply domain knowledge across our sectors, including healthcare data structures, government reporting frameworks, or utility operational data, to deliver contextually grounded and credible analytical work.

Team Leadership & Mentorship

• Provide technical mentorship and day-to-day guidance to junior and core analysts, supporting their growth in analytical methods, communication, and professional effectiveness.
• Review and quality-assure analytical deliverables produced by less experienced team members, providing constructive, specific feedback that raises the overall quality bar of the practice.
• Contribute to onboarding of new analysts by sharing our tools, standards, documentation practices, and client engagement expectations.
• Participate in or lead internal knowledge-sharing sessions, peer learning events, and analytical communities of practice that build collective team capability.
• Model the analytical standards, client orientation, and professional behaviors expected of all members of the Data Analytics team.

Business Development Support

• Contribute to proposals and business development efforts by drafting technical approach narratives, analytical methodology descriptions, and relevant past performance documentation.
• Participate in client discovery conversations and pre-award technical discussions, representing our analytical capabilities with credibility and specificity.
• Document and share project lessons learned, analytical frameworks, and reusable methodologies that strengthen our institutional capability and proposal quality over time.

Qualifications

Required

• Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Public Policy, Public Health, or a related quantitative field. Master's degree preferred.
• 6 to 10 years of progressive experience in data analysis, business intelligence, or applied analytics, with a demonstrated track record of independently leading complex analytical projects.
• Expert-level proficiency in SQL and at least one analytical programming language, Python or R, including experience with libraries for data manipulation, statistical analysis, and visualization.
• 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 communication.
• Demonstrated experience presenting analytical findings to senior client stakeholders and translating technical outputs into clear, decision-ready narratives.
• Experience working with messy, incomplete, or multi-source datasets common in government, healthcare, or regulated industry contexts.
• Demonstrated experience mentoring analysts and contributing to team quality and capability development.

Preferred

• Master's degree in a quantitative discipline such as Statistics, Applied Mathematics, Data Science, Public Health, or a 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 (e.g., GeoPandas, Folium).
• 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.
• Familiarity with dbt, Airflow, or other data pipeline and transformation tooling used in collaboration with data engineering teams.