Key Responsibilities and Required Skills for a Graduate Data Analyst

🎯 Role Definition

As a Graduate Data Analyst, you are the crucial link between raw data and strategic business decisions. You will be empowered to dive deep into complex datasets, unearth critical insights through rigorous analysis, and present compelling data stories that drive growth and innovation. This is a foundational role designed for a recent graduate with a strong quantitative background and a passion for problem-solving. You will have the opportunity to kickstart your career in the dynamic field of data analytics, working alongside a team of experienced mentors and professionals to make a measurable impact on our business.

📈 Career Progression

Typical Career Path

Entry Point From:

Recent University Graduates (Bachelor's or Master's)
Data Analytics or Business Intelligence Internships
Junior Business Analyst or Reporting Analyst roles

Advancement To:

Data Analyst / Business Intelligence Analyst
Senior Data Analyst
Data Scientist

Lateral Moves:

Business Intelligence Developer
Junior Data Engineer

Core Responsibilities

Primary Functions

Extract, cleanse, and manipulate large, complex datasets from various sources such as SQL databases, APIs, and flat files to prepare them for analysis.
Develop, maintain, and automate insightful dashboards and reports using data visualization tools (e.g., Tableau, Power BI, Looker) to track key performance indicators (KPIs) and business metrics.
Conduct in-depth quantitative analysis and statistical modeling to identify significant trends, patterns, and anomalies that inform critical business decisions.
Collaborate closely with cross-functional teams including Product, Marketing, Finance, and Operations to understand their challenges and deliver data-driven, actionable recommendations.
Design, execute, and analyze A/B tests and other controlled experiments to measure the impact of new products, features, and marketing campaigns.
Translate complex analytical findings and statistical concepts into clear, concise, and compelling narratives for non-technical stakeholders through presentations and written reports.
Author and optimize complex SQL queries to perform data extraction, transformation, and aggregation from our data warehouse and relational databases.
Utilize scripting languages like Python or R for advanced data manipulation, statistical analysis, and the automation of repetitive analytical tasks.
Perform exploratory data analysis (EDA) to proactively uncover hidden opportunities, formulate new hypotheses, and guide future analytical projects.
Assist in the development and implementation of foundational predictive models and machine learning algorithms to address business challenges like customer churn or lead scoring.
Document data sources, analytical methodologies, and internal processes thoroughly to ensure transparency, reproducibility, and knowledge sharing across the team.
Monitor data quality and integrity, partnering with data engineering teams to diagnose and resolve data discrepancies and pipeline issues.
Translate ambiguous business questions into well-defined analytical frameworks, hypotheses, and project plans with clear deliverables.
Present findings, insights, and strategic recommendations directly to business leaders and senior management to influence the company's direction.
Conduct deep-dive investigations into specific business areas, such as customer behavior, operational efficiency, or market trends, to provide a detailed understanding of performance drivers.
Support forecasting and strategic planning efforts by providing data-driven projections, what-if scenarios, and opportunity sizing analysis.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis from various business units.
Contribute to the organization's data strategy and roadmap by identifying new data sources and analytical opportunities.
Collaborate with business units to translate data needs into engineering requirements for the data platform team.
Participate in sprint planning, retrospectives, and other agile ceremonies within the data and analytics team.
Stay current with the latest industry trends, tools, and techniques in data analytics and data science to foster continuous improvement.
Assist in the creation of data dictionaries and contribute to the company's data governance and literacy initiatives.

Required Skills & Competencies

Hard Skills (Technical)

SQL Proficiency: Strong ability to write complex, efficient SQL queries to extract and manipulate data from relational databases (e.g., PostgreSQL, SQL Server, BigQuery).
Data Visualization: Hands-on experience creating dashboards and reports in tools like Tableau, Power BI, Looker, or similar platforms.
Scripting & Analysis: Proficiency in a programming language for data analysis, such as Python (with Pandas, NumPy, Scikit-learn) or R.
Statistical Knowledge: Solid understanding of fundamental statistical concepts, hypothesis testing, and A/B testing methodologies.
Spreadsheet Expertise: Advanced skills in Microsoft Excel or Google Sheets, including pivot tables, advanced formulas, and data modeling.
Data Wrangling (ETL): Familiarity with the concepts of extracting, transforming, and loading data between different systems.

Soft Skills

Analytical & Problem-Solving Mindset: An innate curiosity and a structured approach to deconstructing complex problems and finding evidence-based solutions.
Communication & Storytelling: Excellent verbal and written communication skills, with the ability to translate technical findings into a compelling story for diverse audiences.
Attention to Detail: Meticulous and precise in all aspects of work, from data cleaning to final report generation, ensuring accuracy and reliability.
Collaboration & Teamwork: A proactive and positive team player who thrives in a collaborative environment and can work effectively with cross-functional partners.
Adaptability & Eagerness to Learn: A self-starter who is passionate about learning new technologies and analytical techniques in a fast-paced environment.

Education & Experience

Educational Background

Minimum Education:

Bachelor's Degree

Preferred Education:

Master's Degree in a quantitative or technical field.

Relevant Fields of Study:

Computer Science, Statistics, Mathematics, Economics, Engineering, Business Analytics, or a related quantitative field.
Social Sciences with a strong quantitative research component.

Experience Requirements

Typical Experience Range:

0 - 2 years of relevant experience (including internships, co-ops, or academic projects).

Preferred:

Demonstrable experience through one or more internships in a data-focused role.
A strong portfolio of academic or personal projects showcasing data analysis and visualization skills (e.g., on GitHub, a personal website, or Tableau Public).