Adyen N.V. logo

Optimization Data Analyst

Job Overview

Location

Chicago

Job Type

Full-time

Category

Data Science

Date Posted

June 22, 2026

Full Job Description

đź“‹ Description

  • • Analyze large-scale payment datasets to identify performance trends, uncover optimization opportunities, and deliver high-impact recommendations to Adyen’s biggest merchants while driving scalable improvements for the broader merchant base.
  • • Design and build scalable analytics solutions, automation pipelines, and self-service tools to empower internal teams and stakeholders across the Performance Optimization team.
  • • Partner with Product, Data, and Commercial teams to translate complex business challenges into analytical solutions that drive continuous platform improvements and merchant growth.
  • • Lead complex A/B testing and data investigations to validate hypotheses, shape data-driven strategies, and ensure rigorous experimental design across global markets.
  • • Synthesize complex data into compelling, actionable narratives tailored for both technical and non-technical audiences to influence product and business decision-making.
  • • Develop and maintain dashboards and visualizations using tools like Looker or Tableau to communicate key insights and monitor performance metrics in real time.
  • • Design and implement reliable ETL and data pipelines with built-in data validations to ensure accuracy, consistency, and scalability across large datasets.
  • • Contribute to the elevation of analytical standards within the team by mentoring peers, sharing best practices, and promoting technical excellence in data analysis and automation.
  • • Collaborate cross-functionally to align analytical outputs with commercial objectives, ensuring that insights directly inform product roadmaps and merchant-facing strategies.
  • • Maintain version control of code and analytics workflows using Git to ensure reproducibility, collaboration, and auditability of analytical outputs.
  • • Operate independently in a dynamic, global environment while managing multiple priorities and delivering high-quality results under tight deadlines.
  • • Serve as a key business enabler for the Performance Optimization team by positioning data-driven insights as critical drivers of merchant success and platform innovation.

🎯 Requirements

  • • 3–5 years of relevant experience in data analytics, data science, or a similar role, ideally in fintech, payments, or a high-growth tech environment
  • • Strong proficiency in Python, SQL, and PySpark with hands-on experience in large-scale data processing
  • • Ability to design and develop efficient and scalable ETL and data pipelines, including data validations to ensure reliability
  • • Familiarity with big data tools such as Spark and Airflow, and version control using Git
  • • Solid experience with data visualization tools (e.g., Looker, Tableau) and dashboard development
  • • Deep understanding of statistics, hypothesis testing, and data mining

🏖️ Benefits

  • • Annual base salary range of $120,000 - $156,000
  • • RSUs (Restricted Stock Units) as part of compensation package
  • • Office-first work environment with in-person collaboration in Chicago
  • • Opportunity to work with global clients including Meta, Uber, H&M, and Microsoft
  • • Support for professional growth through mentorship and knowledge-sharing within the team
  • • Inclusive culture that encourages diverse perspectives and authentic self-expression

Skills & Technologies

Python
Git
Apache Spark
Remote
$120k-156k

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Adyen N.V. logo
Adyen N.V.
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About Adyen N.V.

Adyen N.V. is a Dutch payment technology company providing an end-to-end platform that processes card, digital wallet, bank transfer and point-of-sale transactions for online and in-store merchants worldwide. The unified platform handles acquiring, risk management, issuing and settlement, offering real-time data analytics and global acquiring licenses. Founded in 2006 and headquartered in Amsterdam, the company serves enterprise clients including Uber, Spotify and Microsoft.

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