Data Engineer (Spark)

15.1k - 21k PLN15 120 - 21 000 PLN/ mies./ mies.B2BB2B (netto)

MidZdalnie·WarszawaFull-time·B2B

#477889·Dodano 5 dni temu·2

Źródło: nofluffjobs.com

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Tech Stack / Keywords

Python SQL Spark CI/CD Kubernetes KafkaNiFiDocker DatabricksMLOpsDevOpsIcebergdbt

Podsumowanie oferty

As a Data Engineer, you will work with a team of technology experts on data engineering projects, including platforms for automotive and aerospace data and telecommunications reporting. The work includes developing data platforms and pipelines using Spark, Python, Airflow, Iceberg, and cloud technologies.

Wymagania

At least 3 years of commercial experience implementing, developing, or maintaining Big Data systems, data governance, and data management processes.
Strong programming skills in Python (or Java/Scala), including clean code and OOP design.
Hands-on experience with Big Data technologies such as Spark, Cloudera, Data Platform, Airflow, Kafka, NiFi, Docker, and Iceberg.
Excellent understanding of dimensional data and data modeling techniques.
Experience implementing and deploying solutions in cloud environments.
Consulting experience, excellent communication and client management skills, and prior direct client interaction as a consultant.
Ability to work independently and take ownership of project deliverables.
Fluent English (at least C1 level).
Bachelor’s degree in technical or mathematical studies.

Nice to have:

Experience with an MLOps framework such as Kubeflow or MLflow.
Familiarity with Databricks and/or dbt.

Obowiązki

Develop and maintain a high-performance data processing platform for automotive data, ensuring scalability and reliability.
Design and implement data pipelines that process large volumes of data in streaming and batch modes.
Optimize data workflows for efficient data ingestion, processing, and storage using Spark, Cloudera, and Airflow.
Work with data lake technologies such as Iceberg to manage structured and unstructured data.
Collaborate with cross-functional teams to understand data requirements and integrate data sources.
Monitor and troubleshoot the platform, ensuring availability, performance, and accuracy of data processing.
Use AWS cloud services to manage infrastructure and scale processing workloads.
Write and maintain Python (or Java/Scala) code for data processing and automation.

Benefity

Language classes.
Sponsored training and conferences, including access to training materials and certifications through partnerships with Databricks and Anthropic.
Team-building events and an integration budget.
Medical and sports packages, eye care, psychotherapy, and coaching.
Work equipment, including a laptop and other necessary devices.
A dedicated buddy for onboarding.

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