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About the Role

As a data engineer, you will work alongside backend engineers and data scientists to build large-scale recommender systems. You will play a key role across the entire ML lifecycle, from designing and implementing robust batch and streaming preprocessing pipelines to enabling efficient model training and distribution for our real-time recommender systems. This role offers a unique synthesis of scale and ML complexity, with significant input into architectural decisions and the implementation of state-of-the-art ML recommender algorithms.

Responsibilities

Design, build, and optimize large-scale ETL, stream, and batch processing pipelines.
Implement and maintain high-throughput/low-latency data stores for real-time recommendations.
Implement and improve data governance mechanisms to ensure data quality and reliability.
Accelerate the pace of innovation by building and improving ML infrastructure, such as training and evaluation frameworks, and experimentation frameworks.
Collaborate with Product Managers, Data Scientists, Engineers, and Analysts across Zalando to create positive customer impact.
Help define team objectives and continuously improve the self-organization of the team.

Requirements

Strong experience applying software engineering principles to data management systems.
Proficient in building robust, scalable data infrastructure using high-quality code, not just SQL.
3+ years of experience in data engineering projects.
Good understanding of distributed data processing mechanisms, with the ability to diagnose and optimize slow-running data pipelines.
Experience with large-scale batch processing and/or stream processing (e.g. Spark, Hadoop, Flink, Storm, Apache Beam, Kafka).
Proficient in either Python or Java.
A strong understanding of distributed databases, especially high-throughput/low-latency data stores (e.g. DynamoDB, Cassandra, or Redis), is highly valued.

Skills

Python
Java
Spark
Hadoop
Flink
Storm
Apache Beam
Kafka
DynamoDB
Cassandra
Redis

Location

Hybrid

Work Type

Hybrid
Remote
Work from abroad

Experience Level

3+ years of experience in data engineering projects

Benefits

Employee shares program
40% off fashion and beauty products sold and shipped by Zalando
30% off Zalando Lounge
Discounts from external partners
2 paid volunteering days a year
Hybrid working model with up to 60% remote per week
Work from abroad for up to 30 working days a year
27 days of vacation a year
Relocation assistance available
Family services, including counseling and support
Health and wellbeing options (including Gympass)
Mental health support and coaching available
Training platform for development
Biannual peer-to-peer review

About the Company

Lounge by Zalando is an online outlet for fashion and lifestyle products in +20 European countries, offering members daily, time-limited sale campaigns with discounts of up to 75% off the recommended retail price.
The company's strength lies in its focus on fashion and lifestyle brands, offering a wide range from sought-after labels to niche brands, international names to trendy luxury brands.
Lounge by Zalando pioneers innovative supply-chain and production processes with brand partners, offering them an impactful and smart solution for selling fashion.
Learn all about Zalando and our values here: https://jobs.zalando.com/en/?gh_src=22377bdd1us
https://www.zalando-lounge.de

Equal Opportunity

Zalando's vision is to be inclusive by design, starting with hiring.
We do not discriminate on the basis of gender identity, sexual orientation, personal expression, ethnicity, religious belief, or disability status.
Candidates are welcome to leave out their picture, age, or marital status from their application.
We only assess candidates on their qualifications and merit.
We want to provide a great candidate experience and offer accommodations as needed.
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