Data Analyst III

The Data Analyst III consults with internal stakeholders to clarify business questions, collect and analyze data, and support data-driven decision-making. This role uses analytics tools to prepare data, apply standard to intermediate analytical techniques, develop visualizations/information products, and communicate findings to business audiences. The Data Analyst III executes on complex projects independently and begins to lead smaller analytics efforts with guidance as needed.

Scope & Key Responsibilities

• Apply analytics best practices (data quality, documentation, reproducibility, and appropriate use of metrics).

• Execute on analytics projects and initiatives independently, managing tasks, timelines, and stakeholder expectations.

• Partner with stakeholders to understand business needs and recommend relevant analyses, KPIs, and metrics to drive insights and decisions.

• Prepare, clean, and transform datasets using appropriate blending, refinement, and validation techniques, including working with large/complex datasets as needed.

• Perform intermediate statistical analysis to identify trends, drivers, and patterns that support business objectives.

• Create clear visual displays of data using tools such as Tableau and/or Power BI; tailor outputs to the audience and decision at hand.

• Combine data and visuals from multiple sources to tell a coherent and compelling story.

• Effectively lead and manage small/operational analytics projects (scoping work, breaking it into tasks, tracking progress, escalating risks).

• Provide support to analytics team members (e.g., peer reviews, ad hoc analysis support, sharing reusable queries or templates).

• Maintain commercial awareness by understanding customers, the market context, and how business changes affect interpretation of results.

• Use approved AI tools to accelerate routine analytics work (e.g., drafting SQL/Python, summarizing findings, creating outlines for decks/documentation) while maintaining accountability for accuracy and completeness.

Qualifications

• Bachelor’s degree holder

• 3–5 years of experience in Data Analytics.

• Ability to understand complex data structures and apply effective data preparation, blending, refinement, and quality techniques (including large datasets).

• Significant experience leveraging SQL and Python for data querying and analysis (data extraction, joins, transformation, and basic performance awareness).

• Experience with visualization tools such as Tableau and/or Power BI; ability to communicate insights through dashboards and presentations.

• Experience applying intermediate statistics to solve business problems.

• Basic knowledge of big data platforms (e.g., Databricks or equivalent) and working with data in modern data environments.

• Strong communication skills: able to present complex issues as clear, actionable insights for non-technical audiences.

• Knowledge of common project management approaches and lifecycles; able to structure work, manage dependencies, and deliver on time.

• Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.

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