Data Analyst II

Position Summary

The Data Analyst II is a capable analytical contributor who independently designs, executes, and delivers data analysis and visualization work across RS21's client engagements. This role is designed for a practitioner who can take an analytical question from problem definition to client-ready findings without day-to-day direction, make sound methodological choices within established frameworks, and communicate results clearly to a range of audiences, technical and non-technical alike.

This person works confidently across RS21's analytical toolchain, produces high-quality reporting and dashboards, applies statistical methods appropriately, and adapts rapidly to the needs of each client context. They are able to provide constructive feedback to junior analysts, contribute to RS21's analytical documentation, and are actively deepening their expertise in the areas that will define their senior-level contribution.

This role is embedded in real client delivery and expected to take full ownership of assigned analytical workstreams. The Data Analyst II does not wait for scope to be handed down; they clarify requirements, propose analytical approaches, flag methodological risks, and keep delivery moving. They understand that the best analysis is the one that is used long after the engagement ends, and they design their work accordingly.

Critically, the Data Analyst II is developing into the kind of analyst RS21 depends on across engagements: someone who delivers accurate, timely, and persuasive analytical work, communicates with genuine client-centricity, and brings enough self-awareness to know when a question requires a methodology or domain expertise that they need to develop.

Key ResponsibilitiesData Analysis and Insight Delivery

• Independently design and execute analytical projects across multi-source datasets; manage data extraction, transformation, quality assessment, and exploratory analysis without requiring direction on approach.
• Write clean, functional Python or R code for data analysis, automation, and reporting tasks; apply software discipline including version control, notebook documentation, and reproducibility practices.
• Write complex SQL queries independently; optimize performance, choose appropriate query patterns by workload, and communicate query logic clearly in documentation and code review.
• Apply statistical techniques including regression, significance testing, sampling methodology, and descriptive analysis to produce findings that are both technically sound and interpretively honest about uncertainty.
• Independently design and publish production dashboards in Tableau, Power BI, or equivalent; connect to multiple data sources, apply sound visualization and UX principles, and govern data model and access design.
• Independently produce compelling data narratives using storytelling tools and visualization frameworks for diverse client and stakeholder audiences; adapt depth, format, and framing to the audience.

AI-Augmented Analytics

• Use AI productivity tools independently to meaningfully accelerate research, analysis, and reporting tasks; apply sound judgment about when AI assistance improves work and when it introduces risk.
• Apply AI-assisted data preparation and pattern detection within established analytical workflows; validate AI-generated outputs critically before incorporating them into client deliverables.
• Stay current on emerging AI-augmented analytical tools and share relevant learnings with the analytics team; help the practice understand where these tools add genuine value.

Client and Stakeholder Engagement

• Manage stakeholder relationships independently for assigned engagements; facilitate analytical meetings, communicate findings clearly, navigate competing interpretations, and keep stakeholders aligned on scope and timeline.
• Produce high-quality client reports, analytical briefings, and status communications; tailor content to audience and ensure clients have the information they need to act on analytical findings.
• Apply established data governance frameworks in client environments, including documenting ownership, lineage, and metadata, under the direction of an engagement lead.
• Anticipate evolving client analytical needs and proactively communicate concerns about scope, data quality, or methodological limitations before they become delivery risks.

Data Quality and Documentation

• Independently validate analytical outputs for accuracy, reproducibility, and appropriate handling of data limitations; document assumptions, caveats, and quality decisions clearly so reviewers and downstream users can trust and build on your work.
• Maintain high-quality analytical documentation in Confluence or equivalent; structure documentation so teammates unfamiliar with an engagement can understand what was analyzed, how, and what it means.
• Contribute to reusable analytical templates, code libraries, and accelerators that help the team move faster on common problem types.

Collaboration and Mentorship

• Provide structured support to junior analysts through hands-on coaching, code review, documentation, and pairing; help them develop the technical and communication habits that will carry them toward independent contribution.
• Collaborate with data engineers and data scientists to ensure analytical work connects to the right data assets and model outputs; understand the full data flow well enough to design analysis around it.
• Partner with project managers to keep analytical delivery aligned with client expectations and contractual constraints; surface scope and timeline risks early with enough context for leadership to act.

QualificationsRequired

• Bachelor's degree in Statistics, Mathematics, Economics, Data Science, Public Policy, or a related quantitative field.
• 3 to 5 years of progressive experience in data analysis or business intelligence, including independent delivery of analytical projects from problem definition through client presentation.
• Proficiency in SQL and at least one analytical language (Python or R), including libraries for data manipulation, statistical analysis, and visualization.
• Advanced proficiency with Tableau, Power BI, Looker, or equivalent BI platforms; able to design, publish, and govern production dashboards independently.
• Strong grounding in applied statistics including regression, hypothesis testing, sampling methodology, and uncertainty quantification.
• Demonstrated ability to communicate analytical findings clearly to non-technical stakeholders, including written reports and live presentations.
• Experience managing stakeholder relationships and delivering analytical work independently across client engagements.

Preferred

• Experience with geospatial analysis tools such as ArcGIS, QGIS, or Python-based spatial libraries.
• Background in advanced analytics, program evaluation, or government data environments.
• Familiarity with longitudinal data, panel data methods, or quasi-experimental evaluation designs.
• Basic understanding of cloud data platforms such as AWS Redshift, Google BigQuery, or Snowflake.
• Background in consulting or multi-client delivery environments where analytical standards and client communication expectations are high.
• Familiarity with AI-augmented analytics tooling and responsible approaches to integrating generative AI into analytical workflows.

Originally posted on Himalayas