Staff Engineer - Data

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.

This is Data Connect

We are hiring for the Data Connect team in Chicago. This team is part of Adyen's Data Solution strategy to enhance our client's understanding of their customers. We're looking for trailblazers with an entrepreneurial mindset to innovate, tinker, and craft something entirely new from the ground up.

We believe in the transformative power of data. Data is not just a byproduct; it's our driving force, shaping the way we understand our business and deliver innovative solutions to our customers. Within our team, you'll tackle high-impact projects that all revolve around understanding our client's customers. These include developing shopper recognition, attribution, and profiling products. You'll work closely with engineering and product teammates to quickly iterate across every phase of the product life cycle, from evaluating product-market fit to release. 

Staff Engineer at Adyen

As a Staff Engineer focusing on data, you will play a critical role in the Data Connect team. This role is pivotal in advancing our data-driven initiatives and contributing to the creation and enhancement of our dynamic data product suite. 

We seek a passionate and experienced Staff Engineer with a unique blend of skills encompassing Data Science, Data Engineering, and Software Engineering. Ideally, they will excel in building robust data pipelines with accuracy and performance and possess a unique mindset—viewing data as a product. We envision someone who can go beyond traditional data handling, exploring innovative ways to transform our data into impactful products that empower our merchants to extract more value from their first-party data.

Do you get excited by building a shopper recognition application for the biggest brands in the world? You will work on a low-latency, highly available service and run fancy graph algorithms in parallel over tens of billions of data points in Spark. Help us push the boundaries and find the optimal trade-offs between latency, recency, quality, and reliability. 

What you'll do:

Staff Engineers are adept at navigating uncertainty, demonstrating comfort in working with unknowns, and excelling in identifying patterns within ambiguous data scenarios. Their ability to thrive in situations where clear answers are not readily available showcases their agility and skill in finding meaningful insights amid uncertainty. You will be helping build the Data Connect team from scratch, starting with Data Scientists and Data Engineers, together with the Engineering and Product leaders. You will report to the VP of engineering in Data Solution.

• You will have a big say in setting the direction of the architecture and the actual solution of our Data Connect product.
• You will be at the forefront of translating business needs into robust data pipelines, designing and implementing data products, and ensuring the overall quality and performance of our data solutions.
• You will accelerate decision-making and act as multipliers and enablers for technical matters across the organization.
• You will have solid engineering principles of performance, code quality, data validation, governance, and discoverability.
• You will be building ETLs/ELTs and pipelines over big data platforms in PySpark and Airflow with a launch-fast and iterative mindset.
• You will think long-term and lean on experience to aid teams that are blueprinting, defining, or redefining architectures and data models.
• You will collaborate closely with Data Scientists, Data Engineers, Backend- and Frontend Engineers to design and build high-quality data products and tooling.
• You will obtain insights by leveraging machine learning algorithms. Examples are: 
  • • Clustering algorithms to group customers/shoppers.
    • Supervised and semi-supervised learning methods for inference on shopper behavior 
    • Graph analysis and representation learning for behavior prediction and monitoring.
• You will mentor and guide the engineers by helping them grow, make better choices, and mature their hard skills.
• You will implement extremely high-quality st