Data Engineer

United States Digital Space LLC; München; Hybrid; EUR 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Birthday off

32 days vacation

Hybrid work scheme

Learning budget

Mental health coaching

Global get-togethers

Sabbatical after 3 years

Cloud-based setup

Zusammenfassung

United States Digital Space LLC is seeking a Data Engineer to shape the future of internal data products. You will build and optimize batch and streaming data pipelines using Python, Spark, and ClickHouse, and orchestrate workflows with Apache Airflow.

You will model data for analytics with SQL and dbt, implement IaC with Terraform, and containerize services with Docker and Kubernetes. Collaboration with analysts and other teams is essential.

Qualifikationen

• 4+ years building and running production data pipelines.
• Python—strong, idiomatic, tested; write readable, debuggable pipelines.
• SQL—advanced; window functions, CTEs, query plans, performance awareness.
• Data modeling and warehousing—dimensional modeling, incremental vs full-refresh, slowly changing dimensions.
• dbt—building, testing, and documenting models in a real project.
• Apache Airflow—authoring DAGs, operational aspects: backfills, retries, SLAs, debugging.
• AWS—hands-on with S3 and data services (Athena, Kinesis, EKS, Lambda).
• Docker & Kubernetes—containerize jobs; understand scheduling issues.
• Git and CI/CD—branching, code review, pipelines.

Aufgaben

• Design, Build and optimize batch and streaming data pipelines with Python, Spark, and ClickHouse.
• Develop and operate workflow orchestration with Apache Airflow to schedule, monitor, and manage pipelines.
• Manage, model and document critical data systems for analytics using SQL and dbt for BI workloads.
• Implement infrastructure-as-code (Terraform) to provision cloud-based data platform components.
• Containerize and deploy services using Docker and Kubernetes (and Helm).
• Collaborate with analysts and product/backend teams to translate requirements into technical designs.

Kenntnisse

Python

SQL

Data modeling

dbt

Apache Airflow

Docker

Kubernetes

Cloud AWS

Spark

Pipelines

Tools

Spark

ClickHouse

dbt

Airflow

Terraform

Docker

Kubernetes

Helm

AWS

S3

Athena

Kinesis

EKS

Lambda

Jobbeschreibung

the company is the world's largest community-driven shopping platform, active in 20+ markets.We help millions make smarter spending decisions across discovery, evaluation, and (re-)purchase by connecting people with the right brands and retailers.

Our 1,000+ team across 10 countries builds products used every day at global scale, where you'll have real ownership and see your impact. Want to shape the destinations people rely on to shop with confidence? Keep reading.

About This Role:

We are looking for a Data Engineer to join our Data Products team. At the company, your role will be instrumental in helping us shape the future of internal data products, creating exceptional user experiences and empowering consumers to make informed and confident shopping decisions. As a key part of our team, you'll be directly involved in building and enhancing the digital destinations our users rely on throughout their shopping journeys.

Your contributions will help shape how millions of consumers interact with our platforms, guiding them to make smart, fair, and rewarding choices.

Our Benefits:

• A culture that values personal and professional development, with internal mobility opportunities.
• A supportive and open-minded team that embraces diverse perspectives and innovative ideas.
• 32 days of paid vacation plus your birthday off, giving you the time you need to recharge.
• A flexible hybrid working scheme to balance work and life.
• Access to a learning budget and internal training to help you grow in your role.
• Mental health coaching to support your well-being.
• Regular global and local get-togethers to celebrate successes and build connections.
• The possibility of taking a sabbatical after three years with the company.
• A cloud-based company setup, providing flexibility and collaboration opportunities no matter where you are.

*These are global benefits that apply to all employees, with additional local perks based on your location.

Responsibilities: In this role, you will:

• Design, Build and optimize batch and streaming data pipelines with strong performance, fault tolerance, and observability using Python, Spark, and Clickhouse.
• Develop and operate workflow orchestration with Apache Airflow to schedule, monitor, and manage data pipelines and transformations.
• Manage, model and document critical data systems for analytics using SQL and dbt to support business intelligence and reporting workloads.
• Implement infrastructure-as-code (e.g., Terraform) to provision and manage cloud-based data platform components.
• Containerize and deploy services using Docker and Kubernetes (and related tooling such as Helm).
• Collaborate with analysts, application teams and other stakeholders to turn requirements into technical designs and delivered solutions.

Your Profile:

We're looking for someone with data engineering experience, who is dedicated to creating exceptional user experiences and driving innovation.

Must have:

• 4+ years building and running production data pipelines.
• Python - strong, idiomatic, tested. You write pipelines that other people can read and easily debug.
• SQL - advanced. Window functions, CTEs, query plans, and the instinct to know why a query got slow.
• Data modeling & warehousing - dimensional modeling, incremental vs. full-refresh strategies, slowly changing dimensions, and the trade-offs between them.
• dbt - building, testing, and documenting models in a real project.
• Apache Airflow - authoring DAGs, plus the operational side: backfills, retries, SLAs, and debugging a failed run.
• AWS - hands-on experience with S3 and the surrounding data services (Athena, Kinesis, EKS, Lambda).
• Docker & Kubernetes - you can containerize a job and understand what happens when a pod won't schedule.
• Git and CI/CD - branching, code review, and pipelines that gate merges.

Nice to have:

• Knowledge of ClickHouse or another columnar OLAP engine (BigQuery, Redshift) table engines, partitioning, and MergeTree tuning are a big plus.
• Good experience with streaming data ingestion technologies such as Kafka, Kinesis, or similar.
• Familiarity with infrastructure as code (IaC) - Terraform, Helm, Ansible.
• Experience with data lake architectures - Parquet, Iceberg, or Delta Lake, and an understanding of layered lake design (bronze silver gold).
• Experience with BI tooling - Apache Superset, Looker, Tableau, or equivalent.
• Strong experience integrating third-party APIs - handling inconsistent schemas, rate limits, and unpredictable failure modes at scale.
• Working knowledge ofApache Spark - PySpark or Scala.

Soft skills:

• Attention to detail - You care about correctness and you build the checks that prove it. In data engineering, a silently wrong number is worse than a loud failure.
• Ownership - you run what you build, and you improve it when needed.
• Clear communication - you can effectively explain in a pipeline to an analyst and a trade-off to a stakeholder.
• Pragmatism - you can tell the difference between the right long-term design and the short-term solution, and you know when each one applies.
• Collaboration - you work well with data engineers, analysts, and other product and backend teams.

Language requirements:

• English - fluent, written and spoken.

Our hiring process:

• TA Call: Meet one of our Talent Experts and get to know the company better.
• Technical Round: Focus on the technical aspects of the role (Through a Live Case), and meet your potential manager.
• Final Round: Meet other Atollians

It varies from 1 to 3 interviews

*Some processes might slightly change according to needs

At the company, we want to ensure that all employees can thrive in an inclusive environment. Our employment opportunities are open to every gender, race, religion, age, sexual orientation, ability, place of origin, or socioeconomic status. We remain committed to a culture of diversity, equity and belonging, where all employees are welcomed, respected, connected, and engaged.

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