Mid-Level
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
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Apply analytics best practices (data quality, documentation, reproducibility, and appropriate use of metrics).
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Execute on analytics projects and initiatives independently, managing tasks, timelines, and stakeholder expectations.
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Partner with stakeholders to understand business needs and recommend relevant analyses, KPIs, and metrics to drive insights and decisions.
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Prepare, clean, and transform datasets using appropriate blending, refinement, and validation techniques, including working with large/complex datasets as needed.
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Perform intermediate statistical analysis to identify trends, drivers, and patterns that support business objectives.
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Create clear visual displays of data using tools such as Tableau and/or Power BI; tailor outputs to the audience and decision at hand.
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Combine data and visuals from multiple sources to tell a coherent and compelling story.
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Effectively lead and manage small/operational analytics projects (scoping work, breaking it into tasks, tracking progress, escalating risks).
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Provide support to analytics team members (e.g., peer reviews, ad hoc analysis support, sharing reusable queries or templates).
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Maintain commercial awareness by understanding customers, the market context, and how business changes affect interpretation of results.
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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
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Bachelor’s degree holder
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3–5 years of experience in Data Analytics.
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Ability to understand complex data structures and apply effective data preparation, blending, refinement, and quality techniques (including large datasets).
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Significant experience leveraging SQL and Python for data querying and analysis (data extraction, joins, transformation, and basic performance awareness).
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Experience with visualization tools such as Tableau and/or Power BI; ability to communicate insights through dashboards and presentations.
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Experience applying intermediate statistics to solve business problems.
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Basic knowledge of big data platforms (e.g., Databricks or equivalent) and working with data in modern data environments.
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Strong communication skills: able to present complex issues as clear, actionable insights for non-technical audiences.
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Knowledge of common project management approaches and lifecycles; able to structure work, manage dependencies, and deliver on time.
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Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.
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