About the Role
We are seeking a motivated and detail-oriented Data Analyst Intern who is eager to learn and contribute to real-world data projects. As an intern, you will support the data team in transforming raw data into meaningful insights, building foundational data pipelines, and delivering accurate reports that enable data-driven decision-making across the organization.
Key Responsibilities
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Assist in building and maintaining data pipelines using Python, Pandas, and Polars, under guidance from senior team members.
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Support data cleaning, transformation, and validation, ensuring high data quality and consistency.
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Help develop and maintain data models used for reporting and analytics.
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Work with analysts and business stakeholders to understand data requirements and translate them into datasets or reports.
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Contribute to automated reporting and dashboard workflows, where applicable.
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Perform exploratory data analysis to identify trends, anomalies, and actionable insights.
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Clearly document data logic, assumptions, and processes for reusability and transparency.
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Take ownership of assigned tasks, proactively flag issues, and suggest improvements rather than waiting for instructions.
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Demonstrate a problem-solver’s mindset—digging deeper into data issues and following through until resolution.
Must-Have Skills
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Good knowledge of Python.
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Working knowledge of SQL (joins, aggregations, CTEs, window functions).
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Familiarity with Pandas for data manipulation.
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Strong analytical and problem-solving skills.
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A go-getter attitude with the ability to take initiative and deliver outcomes, not just complete tasks.
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Willingness to learn new tools and technologies quickly.
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Ability to work independently while taking feedback constructively.
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Good communication skills and strong attention to detail.
Nice to Have
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Exposure to PySpark or Polars.
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Basic understanding of ETL/ELT concepts and data workflows.
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Familiarity with Git or version control systems.
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Awareness of cloud platforms such as Azure or tools like Databricks.
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Fundamental understanding of data structures and algorithms.
