Data Engineer - Payments Data Foundations in Berlin bei Zalando

ABOUT THE ROLE

As a (Senior) Data Engineer within the Payments Data Foundations team, you own the data products including (real-time) data processing pipelines that are used by our internal customers and a variety of stakeholders to flexibly and timely monitor business processes, manage event streams and support decision-making. You provide the data foundation in the cloud to various descriptive and prescriptive analytics performed by product analysts, business analysts, product managers and engineering managers.

INCLUSIVE BY DESIGN

At Zalando, our vision is to be inclusive by design. And this vision starts with our hiring - we do not discriminate on the basis of gender identity, sexual orientation, personal expression, ethnicity, religious belief, or disability status. You are welcome to leave out your picture, age, or marital status from your application. We only assess candidates on their qualifications and merit.

We want to provide you with a great candidate experience. Feel free to inform us of any accommodations you may need, so we can best support you throughout the hiring process.

do.BETTER - our diversity & inclusion strategy: https://corporate.zalando.com/en/our-impact/dobetter-our-diversity-and-inclusion-strategy
Our employee resource groups: https://corporate.zalando.com/en/our-impact/our-employee-resource-groups

WHAT WE’D LOVE YOU TO DO (AND LOVE DOING

• Prepare and contribute to production readiness reviews for the systems owned by your team that you can optimise for cost/efficiency dimension or goal. You ensure quality for operational artefacts (e.g. alerts, metrics, post-mortems, or playbooks).

• Act as a representative of the team in alignments with other delivery teams, customers, or Engineering Managers. You seek and provide advice and criticism for technical approaches and methods, meaning you include others in discussions and designs.

• Add value by leveraging and setting new trends in data engineering through design, architecture and implementation.

• Establish best practices and set up proper tooling to support them such as continuous integration, code quality standards, code reviews, test coverage, post-mortem, retrospectives, etc.

• Work with the newest infrastructure technology like Kubernetes, AWS, Docker, Databricks and a plethora of internal tools for Continuous Integration and Delivery as well as other parts of the data engineering.

• Support our strategy and come up with innovating ideas to build world class data products that are one step ahead of the industry. Contribute to developing a long term vision and the roadmap for a highly scalable data platform and spearhead the technical execution of that vision.

• Consult and help our diverse teams to develop, implement and maintain sustainable, high-performance, growth-ready data-processing and data integration systems. You onboard, coach, and mentor Data Engineers within your team to develop their understanding of domain knowledge, technical expertise, or strengthen their position within the team.

WE'D LOVE TO MEET YOU IF

• Established experience as a data / BI engineer with strong experience building data streams and pipelines using technologies like Apache Kafka, Kinesis, Lambda, Redshift, DynamoDB, SQS, and S3.

• Seasoned problem-solver with strong experience in SQL, Python and Distributed systems.

• Proficiency & expertise in working with Spark, Flink (or a similar real-time stream processing framework like Spark Streaming) and relational or NoSQL databases. Experience with Elasticsearch is advantageous.

• Keep-it-simple approach, with the right amount of foresight in designing an efficient , scalable and flexible architecture for data processing.

• A product-driven mindset, while willing to challenge the status quo when necessary in pursuit of excellence and continuous improvement

• Familiarity with continuous integration and continuous deployment, test-driven development and elastic cloud architectures (AWS)

• A strong understanding of distributed data platform architecture that enables a diverse set of internal customers to work efficiently with data.

• Solid design and implementation experience with very large datasets, distributed applications, GDPR and data retention and archival policies.

• An empathetic team member, who encourages knowledge sharing and also drives technical discussions within the team.

• Strong co