Senior Data Engineer / Tech Lead (Spark)

21k - 28.6k PLN21 000 - 28 560 PLN/ mies./ mies.B2BB2B (netto)

SeniorZdalnie·WarszawaFull-time·B2B

#477893·Dodano 5 dni temu·2

Źródło: nofluffjobs.com

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Tech Stack / Keywords

Python SQL Spark AWSIcebergDocker Kubernetes CI/CD KafkaNiFiDatabricks DevOpsClouderadbt

Podsumowanie oferty

Build and develop scalable data pipelines and modern data processing solutions for a complex data engineering project. The role combines hands-on data engineering with technical leadership, using Python, SQL, Apache Spark, and Apache Iceberg.

Wymagania

At least 5 years of commercial experience in Data Engineering or Big Data projects.
Strong programming skills in Python.
Very good knowledge of SQL.
Strong hands-on experience with Apache Spark and distributed data processing.
Practical experience with workflow orchestration tools, preferably Apache Airflow.
Experience with Apache Iceberg and/or modern lakehouse architectures.
Experience working with Docker and Kubernetes.
Good understanding of data modelling, data pipelines, and data processing architectures.
Experience working with relational databases and/or query engines such as PostgreSQL or Trino.
General understanding of cloud environments and cloud-based data solutions.
Ability to take ownership of technical solutions and contribute to architectural and technical decisions.
Experience supporting other engineers through technical guidance, knowledge sharing, or mentoring.
Ability to work independently and take ownership of project deliverables.
Strong communication and collaboration skills.
Fluent English (at least C1 level).
Bachelor’s degree in technical or mathematical studies.

Obowiązki

Design, develop, and maintain scalable data pipelines using Python, SQL, Apache Spark, and Apache Airflow.
Build and improve data processing solutions based on Apache Iceberg and modern lakehouse architectures.
Work with PyArrow, PyIceberg, PostgreSQL, and Trino.
Take technical ownership of selected areas and contribute to technical and architectural decisions.
Combine hands-on development with technical leadership and knowledge sharing within the team.
Build and maintain containerized workloads using Docker and Kubernetes.
Ensure data pipelines are scalable, reliable, maintainable, and production-ready.
Collaborate with engineers and other stakeholders to understand business and technical requirements and translate them into effective solutions.
Contribute to engineering best practices, including code quality, testing, CI/CD, and automation.
Monitor, troubleshoot, and continuously improve existing data pipelines and platform components.
Use modern development practices, including AI-assisted development, to improve engineering efficiency.

Benefity

Flexible work arrangements, including remote work or work from modern offices and coworking spaces.
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, and well-being support services, including psychotherapy and coaching.
Full work equipment, including a laptop and other necessary devices.
Additional paid leave.
In-house trainings.
Referral bonus.
Free coffee.

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