About the Opportunity

We are building a data-driven organization where important decisions are expected to begin with evidence, not assumptions.

To support that ambition, we are seeking a highly analytical and intellectually curious Junior Data Analyst to join a growing data and business intelligence function.

This is not a reporting-only position.

You will work with real business questions across customers, products, operations, finance, marketing, growth, and digital experiences, transforming raw information into analysis that helps teams understand what is happening, why it is happening, and what they should do next.

You may investigate why customer conversion changed unexpectedly, identify operational bottlenecks, analyze product adoption, develop a new executive KPI, evaluate campaign performance, investigate unusual data patterns, or help determine whether a new business initiative is producing its intended results.

You will work alongside experienced analysts, data engineers, business leaders, and technology teams while developing hands-on expertise in SQL, Python, business intelligence, data visualization, experimentation, data quality, and modern AI-assisted analytical workflows.

We are particularly interested in candidates who are early in their careers but demonstrate unusually strong analytical instincts.

You do not need to know everything on day one.

You do need to be curious enough to ask why, disciplined enough to validate your answer, and clear enough to explain what the data actually means.

Essential Duties and Responsibilities

Business & Data Analysis

Analyze structured datasets to answer important business questions.
Translate stakeholder questions into measurable analytical problems.
Identify trends, patterns, anomalies, and performance drivers.
Conduct exploratory and diagnostic analysis.
Compare performance across products, customers, markets, channels, and time periods.
Develop actionable findings rather than simply reporting numbers.
Validate analytical conclusions before communicating recommendations.

SQL & Data Exploration

Write SQL queries to extract, combine, filter, and analyze data.
Work with relational data across multiple business systems.
Perform joins, aggregations, CTEs, window functions, and other analytical operations as experience develops.
Validate query outputs against source systems.
Create reusable analytical datasets.
Partner with Data Engineering when new data sources or transformations are required.

Business Intelligence & Visualization

Build and maintain dashboards using tools such as Power BI, Tableau, Looker, or comparable platforms.
Translate complex datasets into clear visual stories.
Develop KPI scorecards for business teams.
Improve existing dashboards and reporting.
Ensure metric definitions remain consistent.
Design reporting around decisions rather than unnecessary visual complexity.

Python & Analytical Automation

Use Python where appropriate for analysis and automation.
Work with libraries such as pandas, NumPy, or comparable analytical frameworks.
Automate repetitive analytical tasks.
Perform data cleaning and transformation.
Support statistical analysis and larger analytical workflows.
Develop technical capabilities through mentorship and practical project work.

KPI & Performance Measurement

Help define meaningful business metrics.
Develop recurring performance reporting.
Monitor changes in key indicators.
Investigate unexpected performance movements.
Partner with stakeholders to understand metric definitions.
Help establish reliable sources of truth for important business KPIs.

Data Quality

Identify incomplete, inconsistent, duplicated, or unexpected data.
Conduct validation before analysis is distributed.
Document data-quality issues.
Partner with Data Engineering and system owners on root-cause resolution.
Help improve confidence in enterprise reporting.
Maintain documentation around important datasets and metric definitions.

Customer & Product Analytics

Analyze customer behavior and product usage.
Support segmentation and cohort analysis.
Evaluate adoption, engagement, conversion, retention, and churn.
Identify behavioral patterns associated with customer outcomes.
Support Product teams with data-driven insights.
Help evaluate new features and digital experiences.

Experimentation & Measurement

Support A/B tests and controlled experiments.
Help define success metrics before initiatives launch.
Analyze experiment results.
Partner with Product, Marketing, or Growth teams on interpretation.
Distinguish correlation from causation where appropriate.
Document methodology and assumptions.

AI-Assisted Analytics

Use approved AI tools to accelerate appropriate analytical workflows.
Explore AI-assisted SQL development, documentation, research, and analysis.
Validate AI-generated analytical outputs before use.
Identify opportunities where automation can improve analytical productivity.
Maintain appropriate data privacy and governance standards.
Develop practical understanding of how AI is changing modern analytics.

Stakeholder Reporting

Translate analysis into concise business recommendations.
Develop presentations and analytical summaries.
Explain findings to non-technical stakeholders.
Clearly communicate limitations and assumptions.
Respond to follow-up questions with additional analysis where appropriate.
Build credibility through accuracy and consistency.

Job Qualifications and Requirements

Bachelor’s degree in Data Analytics, Statistics, Mathematics, Economics, Computer Science, Information Systems, Finance, Business Analytics, or a related quantitative discipline.
Approximately 0–3 years of professional analytical experience; exceptional recent graduates with relevant internships, projects, or technical portfolios are encouraged to apply.
Working knowledge of SQL.
Familiarity with Excel or Google Sheets for analytical work.
Exposure to Power BI, Tableau, Looker, or another visualization platform.
Basic knowledge of Python or R is strongly preferred.
Understanding of basic statistical concepts.
Ability to organize and analyze structured datasets.
Strong attention to accuracy and data quality.
Ability to communicate analytical findings clearly.
Strong written and verbal communication skills.
Demonstrated curiosity and willingness to learn unfamiliar business domains.

Particularly Valuable Experience

Experience or academic exposure involving:

SQL
Python
Power BI
Tableau
Looker
Snowflake
BigQuery
Databricks
AWS, Azure, or Google Cloud
dbt
Product analytics
Customer analytics
Financial analytics
Marketing analytics
A/B testing
AI-assisted analytics

Relevant internships, university projects, GitHub portfolios, case competitions, certifications, or independent analytical projects may be considered alongside traditional professional experience.

Personal Capabilities and Qualifications

Analytical Curiosity

You do not stop at discovering that a metric changed—you want to understand why.

Accuracy

You recognize that an attractive visualization is worthless if the underlying calculation is wrong.

Business Thinking

You are interested in the decision behind the data request, not merely the requested spreadsheet.

Learning Agility

You can quickly learn unfamiliar datasets, tools, and business concepts.

Healthy Skepticism

You validate surprising results rather than immediately assuming they are correct.

Communication

You can explain analytical findings without requiring stakeholders to understand the technical work behind them.

Ownership

You take responsibility for the quality and completeness of your analysis.

Collaboration

You are comfortable learning from Data Engineers, Senior Analysts, Product Managers, Finance teams, and business leaders.

Strategic Support

As your capabilities develop, you may contribute to broader initiatives involving:

Enterprise analytics
Executive dashboards
Customer intelligence
Product analytics
Financial performance
Marketing effectiveness
Operational efficiency
Forecasting
Data-quality improvement
Digital transformation
AI-enabled analytics
Automation
Customer segmentation
Experimentation
Strategic planning
Business-performance measurement

Exposure across multiple functions is intentionally built into the role to develop a broader understanding of how data influences enterprise decisions.

Working Conditions

Remote or flexible working arrangements may be available depending on organizational requirements.
Regular collaboration with Data, Technology, Product, Finance, Marketing, Operations, and other business teams.
Work is primarily performed in a professional digital environment.
Multiple analytical requests and projects may occasionally run simultaneously.
Periodic meetings across different time zones may be required.
The role requires appropriate handling of confidential business, customer, financial, and operational information.
Candidates are expected to follow applicable data privacy, security, and governance requirements.

Job Function

Primary Function: Data Analytics & Business Intelligence

Core Areas:

Compensation & Benefits

The anticipated compensation range for this position is:

$165,000 – $192,000 annually

Final compensation will consider analytical capabilities, technical proficiency, relevant internships or professional experience, academic background, portfolio quality, geographic considerations, and overall qualifications.

The broader total rewards package may include:

Annual performance incentives
Equity or long-term incentives where applicable
Comprehensive medical, dental, and vision coverage
Retirement savings with employer contributions
Generous paid time off and company holidays
Remote and flexible work arrangements
Data and technology certification support
Professional development and mentorship
Conference and technical-learning opportunities
Home-office and technology resources
Wellness and family-support programs

Why Join Us

Early-career analysts are often hired to maintain someone else’s reports.

This opportunity is designed differently.

You will learn how experienced teams use data to answer real questions involving customers, products, growth, operations, finance, and strategy.

You will have room to strengthen your SQL, develop Python skills, build business intelligence solutions, work with modern cloud data platforms, explore AI-assisted analytical workflows, and learn directly from experienced technical and business professionals.

Most importantly, you will learn the difference between producing data and creating insight.

Your development will follow a path from:

Raw Data → Analysis → Insight → Recommendation → Business Decision

For an early-career analyst with strong quantitative instincts, genuine curiosity, and the ambition to develop into a highly capable Data Analyst, Business Intelligence Analyst, Product Analyst, Analytics Engineer, or Senior Data Analyst, this role provides an unusually strong foundation for growth.

Junior Data Analyst