Data Analyst II

Data 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.