Data Engineer (High-Scale Distributed Systems) - Warsaw, hybrid - B2B/UoP

EER Poland•Capon Bridge, WV

4h•Hybrid

About The Position

This company is a fast-growing technology company building advanced AI-driven solutions powered by deep learning and large-scale data processing. The organization develops proprietary predictive models that enable real-time, data-driven decision-making across complex markets. We are looking for an experienced Data Engineer who wants to play a key role in designing and scaling robust data infrastructure in a production environment.

Requirements

4+ years of professional experience with Python and SQL.
Proven experience building and maintaining data pipelines (ETL/ELT).
Hands-on experience with orchestration tools such as Airflow, Dagster, or similar.
Strong experience with distributed data processing frameworks (Spark, Dask, Beam).
Experience with at least one major cloud platform (AWS, GCP, or Azure); BigQuery is a plus.
Solid understanding of Docker/Kubernetes and CI/CD pipelines.
Strong software engineering fundamentals (data structures, algorithms, testing, code quality).
Experience with data testing and monitoring tools (pytest, Great Expectations, observability tools).

Nice To Haves

Experience with dbt, ClickHouse, or Ray.
Familiarity with Python data libraries (pandas, NumPy, Apache Arrow, Jinja).
Background in functional programming.

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming).
Optimize distributed data processes for performance and efficiency.
Ensure data quality, consistency, and reliability across workflows.
Work with cloud-native architectures and modern data platforms.
Implement and manage orchestration frameworks (e.g., Airflow, Dagster).
Collaborate closely with data science, research, and business teams.
Develop and maintain CI/CD processes and data testing frameworks.
Monitor, troubleshoot, and continuously improve pipeline performance and stability.

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