Junior Data Analyst - New York

ARQ•New York, NY

11d•Onsite

About The Position

As a Data Analyst at ARQ, you’ll help us understand our business, customers, and operations through rigorous analysis and thoughtful storytelling. You’ll explore data, detect patterns, and build the foundations for decisions that shape the future of our products and company. This is a role for someone early in their analytics career who wants to grow — learning how data drives product, financial, and strategic decisions in a fast-paced fintech environment.

Requirements

1–3 years of experience in analytics, data, or operations (internships count)
Strong SQL skills for querying and exploring datasets
Comfortable reading code and writing basic Python scripts for data manipulation, automation, and analysis
Experience contributing to a shared analytics codebase or modern data stack (e.g., Snowflake/BigQuery + dbt + Git workflow)
Analytical curiosity and a structured approach to problem solving
Good communication skills - able to explain insights clearly and visually
Initiative to automate repetitive tasks and improve reporting workflows
Interest in fintech, data-driven decision-making, and machine learning concepts
Ambitious, high-potential mindset with evidence of strong achievement, fast progression, or outstanding academic/professional performance

Nice To Haves

STEM degree preferred (e.g., Mathematics, Computer Science, Engineering, Physics, Economics, or related fields)
Experience with BI or visualization tools (Metabase, Looker, etc.)
Familiarity with dbt for building and maintaining data models, writing tests, and managing transformations
Exposure to fraud, financial, or product analytics (any domain welcome)
Spanish/Portuguese proficiency for internal and external communications.

Responsibilities

Explore and Analyze Data: Investigate user behavior, performance trends, and key metrics to uncover what’s driving changes.
Identify Anomalies and Opportunities: Spot unusual patterns or spikes and help the team understand their root causes.
Reporting & Automation: Build and maintain dashboards and automated reports that make data accessible and reliable.
Decision Support: Translate analytical findings into clear recommendations for product and operational improvements.
Metric Design: Help define and refine the KPIs that matter most for our business and customer experience.
Documentation: Keep analyses, methodologies, and assumptions well-documented and reproducible.
Modeling & Experimentation: Contribute to basic modeling tasks and structured experiments under guidance from senior analysts or data scientists.
Continuous Learning: Expand your analytical toolkit and develop product sense through real-world problem solving and mentorship.