Data Analyst Interns

Key Responsibilities

1. Data Ingestion & Data Pipeline Support

Assist in collecting and ingesting data from multiple sources such as APIs, databases, and external datasets

Support the development and maintenance of data pipelines and ETL workflows

Help ensure data integrity, consistency, and reliability across systems

2. Data Preparation & Processing

Clean, transform, and organize datasets for analysis and reporting

Assist in preparing datasets for dashboards, analytics models, and internal reporting

Perform data validation and quality checks

3. Data Analytics & Insights

Conduct exploratory data analysis to identify trends, patterns, and anomalies

Assist in building reports and visualizations for internal stakeholders

Help translate raw data into actionable insights for product, marketing, and operational teams

4. Tools & Data Infrastructure Support

Work with data tools and platforms to support data ingestion and analytics workflows

Assist in documenting datasets, workflows, and data structures

Support the team in improving data accessibility and reporting efficiency

5. Cross-Functional Collaboration

Collaborate with engineering, product, and business teams to understand data requirements

Support requests for data analysis and reporting

Communicate findings and insights clearly through reports or dashboards

Required Skills & Tools

Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Information Systems, or related fields

Basic knowledge of SQL and data querying

Familiarity with Python, R, or other data analysis tools is a plus

Exposure to data visualization tools (Power BI, Tableau, Looker Studio, etc.) is advantageous

Understanding of data structures, databases, and data processing concepts

Strong analytical thinking and attention to detail

Self-starter with the ability to work independently, manage tasks proactively, and adapt in a fast-paced environment

Preferred Qualifications

Exposure to AI-assisted coding or scripting tools (e.g., Python AI libraries, LLM APIs, or AI copilots) to support data ingestion, automation, or analytics workflows

Familiarity with using AI tools to accelerate coding, data processing, or analysis tasks

Comfortable self-learning new tools and technologies, particularly AI or automation tools

Ability to research, experiment, and independently implement scripts using documentation and online resources

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