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Optimization Data Analyst

Job in Northern, Floyd County, Kentucky, USA
Listing for: EngineersOfAI
Full Time position
Listed on 2026-08-24
Job specializations:
  • IT/Tech
    Data Analyst, Data Science Manager, Data Engineering
Salary/Wage Range or Industry Benchmark: 132000 - 178000 USD Yearly USD 132000.00 178000.00 YEAR
Job Description & How to Apply Below
Location: Northern

This is Adyen

Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition.

For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster.

We are seeking an experienced Optimization Data Analyst to join our team in San Francisco to play a critical role in driving scalable optimizations and advanced analytics across Adyen's global Performance Optimization team. As an Optimization Data Analyst, you will independently lead complex analyses, support automation and experimentation projects, and collaborate with commercial and product stakeholders to deliver actionable insights that maximize merchant growth.

This role is ideal for an experienced analyst looking to deepen technical impact while influencing product and business outcomes at scale.

What You'll Do
  • Data-Driven Optimization:Analyze large-scale payment datasets to identify performance trends, uncover optimization opportunities, and deliver high-impact recommendations to our biggest customers, while identifying and driving scalable opportunities that benefit the broader merchant base.
  • Scalable Tooling & Automation:Design and build scalable analytics solutions, automation pipelines, and self-service tools that empower internal teams and stakeholders.
  • Cross-Functional Collaboration:Partner with Product, Data, and Commercial teams to translate business challenges into analytical solutions, driving continuous platform improvements.
  • Advanced Problem-Solving & Experimentation:Lead complex A/B testing and data investigations, shaping data-driven strategies and validating hypotheses with analytical rigor.
  • Strategic Data Storytelling:Drive strategic discussions by synthesizing complex data into compelling narratives, tailoring insights for technical and non-technical audiences, and positioning the Performance Optimization team as a key business enabler.
  • Mentorship & Best Practices
    :
    Support knowledge-sharing within the Performance Optimization team, contributing to the elevation of analytical standards and technical excellence.
Who You Are
  • 3-5 years of relevant experience in a data analytics, data science, or 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 for datasets, including the use of data validations to ensure reliability.
  • Familiarity with big data tools (Spark, 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.
  • Strong cross-functional collaboration and stakeholder management skills, with the ability to influence product and business outcomes.
  • Proactive, self-directed, and able to manage multiple priorities in a dynamic, global environment.
  • Excellent communication and storytelling skills, translating data insights into business value.

Compensation The annual base salary range for this role is $132,000 - $178,000, plus RSUs; to learn more about our compensation philosophy, please

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