Data engineer - Platform Engineering Focus Poland

Zoolatech; Wrocław; Híbrido; PLN 180 000 - 260 000; Tempo integral

Resumo da oferta

Nordstrom is seeking an Orchestration Team Engineer to develop and maintain scalable data platforms that simplify ETL/ELT for engineers, data scientists, and analysts. You will enable secure, self‑service data workflows across sources like Kafka, S3, GCS, BigQuery and Postgres.

You will work with Python, Airflow, Kubernetes, Terraform, and cloud data services, delivering robust data pipelines and governance capabilities for a large retail ecosystem.

Qualificações

• Proficiency in SQL and BigQuery for data modeling and analysis.
• Experience with ETL/ELT processes and modern transformation frameworks such as dbt.
• Strong Python skills to build CLIs, services and automation.
• Experience with cloud data platforms, especially GCP (BigQuery, Dataproc Serverless, GCS).
• Knowledge of Terraform for infrastructure as code and orchestration platforms.
• Experience building data pipelines with Airflow and Kubernetes.
• Familiarity with streaming/data event systems like Kafka.

Responsabilidades

• Platform Development: evolve and maintain orchestration platforms to simplify ETL/ELT operations.
• Platform Support: provide production and on‑call support, coordinating with onsite and offshore teams.
• Infrastructure as Code: manage cloud data infrastructure declaratively using Terraform.
• Integration: connect to diverse data sources and destinations with high performance.
• Data Governance & Metadata: build metadata, cataloging, and lineage for trustworthy data.

Conhecimentos

SQL

BigQuery

Python

Airflow

Kubernetes

Terraform

GCP

Kafka

Data governance

CI/CD

GitHub Actions

LLMs tooling

Ferramentas

dbt

Looker/LookML

DataHub/Collibra/Amundsen

Spark/Java/Scala

Descrição da oferta de emprego

Our partner is one of the largest retail e-commerce companies in North America, serving millions of customers each year and ensuring a secure and seamless shopping experience across digital and in‑store channels.

We are looking for skilled and motivated engineer Data engineer to join the innovative team.

The Orchestration team manages platforms within Insights Delivery Technology organization and offers self‑service solutions designed to empower engineers, data scientists, and analysts to seamlessly perform ETL/ELT operations on data from diverse sources such as Kafka, S3, GCS, BigQuery, and Postgres, and write processed data back to these sources via batch or real‑time processes. Increasingly, the team also owns the metadata, governance, and data‑lineage layer that makes this data discoverable and trustworthy across the company.

As an Orchestration Team Engineer, you will play a critical role in developing and maintaining this robust platform, ensuring that users can efficiently extract actionable insights without being burdened by the complexities of big data technologies. Your work will enable the client to scale its operations while keeping technical demands on non‑engineering staff to a minimum.

• Platform Development: Evolve, design, and maintain our orchestration platforms which aim to abstract the complexities of big data technologies and simplify ETL/ELT operations for users
• Platform Support: Perform ongoing production support and on‑call support duties. Work with both onsite and offshore resources daily to resolve incidents and issues.
• Infrastructure as Code: Provision and manage cloud data infrastructure declaratively with Terraform, delivering self‑service platform capabilities through automated, version‑controlled pipelines.
• Integration: Develop seamless connectivity to diverse data sources and destinations, ensuring high performance and reliability.
• Data Governance & Metadata: Build and operate metadata, cataloging, and data‑lineage capabilities that make data across Nordstrom discoverable, trustworthy, and well‑governed — including integrations across transformation, streaming, BI, and ML platforms.
• Scalability: Design solutions that are scalable and resilient, capable of handling large data volumes, high velocity, and diverse data formats.
• Innovation: Stay up-to-date with emerging big data, ETL, and AI‑assisted tooling, incorporating them into the framework as needed to enhance functionality and user experience.
• Documentation: Create and maintain comprehensive documentation, ensuring users can easily understand and utilize the framework
• Experience building, developing, and maintaining data platforms, particularly self‑service platforms that enable engineering teams, data scientists, and analysts to configure and manage data processing workflows with minimal manual intervention.
• Proficiency in SQL (e.g., BigQuery).
• Solid understanding of ETL/ELT processes, data pipeline architecture, and modern transformation frameworks such as dbt.
• Strong programming skills in Python, with the ability to build and maintain CLIs, services, and automation.
• Experience with cloud data platforms, primarily GCP (BigQuery, Dataproc Serverless, GCS); AWS experience a plus.
• Experience with Infrastructure as Code — particularly Terraform — for provisioning and managing cloud data infrastructure.
• Experience building and orchestrating data pipelines with Airflow and Kubernetes.
• Experience with streaming / event data using Kafka (e.g., Confluent Cloud).
• Working understanding of data governance concepts — metadata management, data cataloging, and data lineage.
• Familiarity with CI/CD and Git‑based workflows (GitHub Actions) for platform and pipeline delivery.
• Proficiency with AI‑assisted developer tooling — LLMs, the Model Context Protocol (MCP), and "copilot"-style productivity tools — and a track record of using them to work more effectively.
• Excellent problem‑solving and communication skills, with the ability to work effectively in a collaborative environment.

Nice to Have

• Hands‑on experience with a metadata / data‑catalog / lineage platform such as DataHub (or Collibra, Amundsen, or similar).
• Experience with BI / semantic‑layer tools such as Looker / LookML.
• Experience with data quality, observability, SLOs / error budgets, and on‑call operations for data platforms.
• Familiarity with distributed processing frameworks such as Spark; Java / Scala a plus.

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