DataOps Engineer

WHO WE ARE

At Trustly , we're building a smarter, faster, and more secure financial future by revolutionizing the world of payments. As a global leader in Open Banking Payments, we are establishing Pay by Bank as the new standard at checkout, providing unparalleled freedom, speed, and ease to millions of consumers and merchants worldwide.

Our Ambition: To build the world’s most disruptive payment network and redefine what the payment experience should feel like.

Trustly is a global team of innovators, collaborators, and doers. If you are driven by a strong sense of purpose and thrive in a dynamic, entrepreneurial, and high-growth environment, join us and be part of a team that’s transforming the way the world pays.

About the team Trustly 's DataOps team is responsible for delivering the data generated by the application to interested areas, as well as data from APIs and other tools. All this thought in a safe, structured, scalable and generic way, because we work with multiple environments (in different regions) and we need to maintain consistency. We work with both the batch layer (using Airflow) and the streaming layer (Kafka). We help areas in process automation to deliver data more quickly and reliably. We are also concerned with the quality of the data (Data Quality), creating an observability layer for the data to act in a preventive and immediate way to the inconsistencies and failures of our processes. We also work on the delivery of products and services to facilitate the use and consultation of our data, such as maintaining tools such as Redshift and QuickSight. Last but not least, we interacted with the Data Science area to provide the necessary support and infrastructure for the production of models. To achieve our goals, we follow good code and development practices, apply end-to-end encryption in all our processes, use infrastructure as code, etc.

Our work makes it possible for teams to have full capacity to work with the company's data. The Data Analytics area develops several internal processes to facilitate the delivery of more structured data to the business areas, developing views and reports that leverage the data in decision making. In addition, we were able to provide more productivity to the areas with the automation of processes, guaranteeing the dependencies and leaving the areas more focused on their activities. We make data available in a secure manner for use by the Risk & Data Science areas, to facilitate the training and production of models that have a direct impact on the conversion of our transactions and the fight against fraud. Our work is summarized in delivering data that will be decisive for the areas in their processes and also in making it a valuable asset as a parameter of our growth and decision-making.

We still have many challenges ahead, such as having a robust monitoring and visibility system on top of the lake, using dedicated tools to do this c