Werde Teil des Zalando Teams: Senior Data Engineer - Personal Relevance (all genders) in Berlin

Zalando

Berlin

Stellenausschreibung

Location

Berlin

Contract

Full time

Job Category

IT Consulting & Operations

THE ROLE & THE TEAM

Lounge by Zalando is the fastest growing part of Zalando, now turning over billions of euros each year. That said, there is so much untapped potential with respect to personalization and recommendation.

You will be the senior data engineer, leading others to build a large scale recommender system. We are the largest online fashion retailer in Europe, as such we need an exceptional engineer who can write Machine Learning pipelines in Spark at a global scale. You will own everything from batch / stream preprocessing, model training, and model / feature distribution to the real time recommender system.

You will have full autonomy to direct architectural decisions, and help to implement state of the art ML recommender algorithms. There are very few roles which offer this unique synthesis of scale and ML complexity.

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)

Provide critical data infrastructures like large-scale ETL processes, DWHs, stream processing and batch processing pipelines, large-scale high-throughput/low-latency data stores, data governance mechanisms.
Improve the pace of innovation by building and improving ML infrastructures such as training/evaluation frameworks, experimentation frameworks.
Collaborate with brilliant Product Managers, Data Scientists, Engineers and Analysts across Zalando to create positive customer impact together.
Help define our team's objective. Continuously improve the self-organization of the team.

WE'D LOVE TO MEET YOU IF

A Software Engineer specialized in data management. You should be experienced in leveraging software engineering principles to design systems and writing high quality code – not just SQL.
3+ years of experience in large-scale data engineering projects.
Solid experience with large-scale batch processing and/or stream processing (e.g. Spark, Hadoop, Flink, Storm, Apache Beam, Kafka.)
Good understanding of distributed data processing mechanisms. You should be able to diagnose and optimize a slow running data pipeline.
Proficient in either Python or Java.
Excellent knowledge of distributed databases, especially high-throughput/low-latency data stores like DynamoDB, Cassandra, Aerospike, Redis etc. is a strong plus.

OUR OFFER

Zalando provides a range of benefits, here’s an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.

Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Zalando Lounge, discounts from external partners
Two paid volunteering days a year
In a hybrid working model with 60% remote per week, actual practice is up to each team to best support their collaboration
Work from abroad for up to 30 working days a year
27 days of vacation a year to start
Relocation assistance available (subject to prior agreement)
Family services, including counseling and support
Health and wellbeing options (including Gympass)
Mental health support and coaching are available
Drive your development through our training platform and biannual peer-to-peer review