Data Analyst

Job Description

Requirements

• Bachelor’s degree in Data Science, Statistics, Computer Science, Actuarial Science, Economics, or a related quantitative field.
• 3+ years of experience in data analysis, business intelligence, or a related analytical role.
• Demonstrated proficiency in SQL and at least one programming language (Python or R) for data manipulation and analysis.
• Hands-on experience building dashboards and visualizations using BI tools (Amazon QuickSight, Tableau, Power BI, or similar).
• Strong understanding of data quality principles, validation logic, and data cleaning workflows.
• Excellent written and verbal communication skills, with the ability to translate complex data into clear insights.
• Familiarity with Amazon QuickSight specifically (templates, calculated fields, parameters).
• Track record of creating reusable templates, SOPs, or knowledge base documentation for analytics teams.
• Familiarity with survey design tools (ODK, SurveyCTO, KoboToolbox) and field data collection workflows.
• Analytical Rigor: Ability to identify data anomalies, design validation rules, and ensure accurate outputs.
• Visual Communication: Skill in designing intuitive dashboards and visualizations that drive decision-making.
• Ownership Mindset: Proactive accountability for deliverable quality and team standards.
• Collaboration: Willingness to peer-review, share knowledge, and contribute to a culture of continuous improvement.
• Mentoring & Leadership: Ability to guide junior team members, provide constructive feedback, and build capability.
• PERFORMANCE INDICATORS
• On-time delivery of client-facing dashboards and reports for assigned projects.
• Dashboard adoption and client satisfaction scores.
• Quality and completeness of Analytics & Reporting Knowledge Projects.
• Timeliness and thoroughness of peer reviews conducted on team deliverables.

Responsibilities

• The Data Analyst serves as a full-stack project lead responsible for independently managing agricultural data projects end-to-end, while also owning cross-team analytics, reporting, and dashboarding standards. This is a dual-mandate role: you will lead your own projects (running data quality checks, producing analyses, delivering client-facing reports and dashboards) and simultaneously define what excellent analytics and visualization looks like across the entire team.
• This role is ideal for a data professional who combines strong analytical skills with a passion for visual storytelling and a desire to shape team-wide standards and mentor junior talent.
• A. Project Leadership (Assigned Projects)
• Lead end-to-end execution of assigned agricultural data projects, from data quality checks through final client deliverables.
• Create and monitor data quality checks using established DQC frameworks, ensuring data integrity across all project phases.
• Produce project-specific reports, analyses, and  dashboards for client and internal consumption.
• Manage and assign daily tasks to one Junior Data Analyst, ensuring timely completion and quality of work.
• Step in to execute DQCs directly when necessary.
• Own all client-facing deliverables for assigned projects (subject to peer and management review).
• Manage field team orientation and training on DQC frameworks for assigned projects.
• B. Analytics & Dashboarding Standards (Cross-Team)
• Design and build Amazon QuickSight dashboards for all team projects, regardless of project lead assignment.
• Create and maintain reusable dashboard templates, visualization standards, and reporting best practices.
• Define and enforce formatting standards for all team reports and client-facing analytics outputs.
• Peer-review reports and visualizations produced by other team analysts to ensure consistency and quality.
• Serve as the team’s final authority on analytics and dashboard quality standards.
• Maintain the Analytics & Reporting Knowledge Base as a living resource for the team.
• Train the entire team on QuickSight and visualization tools and best practices.
• Build automated reporting pipelines to reduce manual effort and improve consistency.
• C. Review & Mentoring
• Review DQC execution, reports, and analyses produced by peer analyst on their projects.
• Accept reciprocal review of your own work by the Associate Manager or peer analyst.
• Directly mentor the Junior Data Analysts.