Data platform engineer
Joins the Orchestration team building a self-service data platform (ETL/ELT, metadata, governance, lineage) for a large North American retail e-commerce client, working with Python, SQL/BigQuery, GCP, Airflow, Kubernetes, Terraform, and Kafka. Based in Wrocław with production support and on-call duties.
- Compensation
- Not disclosed
- Location
- Wrocław, Poland
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Experience building, developing, and maintaining data platforms, particularly self-service platforms
- 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, including building and maintaining CLIs, services, and automation
- Experience with cloud data platforms, primarily GCP (BigQuery, Dataproc Serverless, GCS); AWS a plus
- Experience with Infrastructure as Code, particularly Terraform
- 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, data lineage
- Familiarity with CI/CD and Git-based workflows (GitHub Actions)
- Proficiency with AI-assisted developer tooling — LLMs, Model Context Protocol (MCP), copilot-style tools
- Excellent problem-solving and communication skills in a collaborative environment
Nice to have
- Hands-on experience with a metadata / data-catalog / lineage platform such as DataHub, Collibra, or Amundsen
- 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
Full description
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.