Data Analyst III

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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