data engineer in data platforms

Vention

Łódź

Vor Ort

PLN 102.000 - 122.000

Vollzeit

14 Tage+

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Benefits dieser Stelle

Private medical care
Kafeteria MyBenefit credits
Multisport membership
Glasses reimbursement
Referral bonuses
AI tools licenses (GitHub Copilot, etc

Zusammenfassung

Vention in Poland is seeking an experienced Data Engineer to design and maintain scalable data pipelines and analytics-ready data stores. You will work across cloud platforms, Spark-based processing, and collaborate with cross-functional teams to turn business needs into reliable data solutions.

This role offers hands-on tooling with modern data architectures and opportunities to contribute to AI-driven projects and data-driven decision making within a dynamic engineering team.

Qualifikationen

  • 4+ years of experience in Data Engineering delivering end-to-end data solutions.

Aufgaben

  • Build, optimize, and maintain scalable ETL/ELT pipelines on cloud platforms.
  • Integrate data sources ensuring data quality, reliability, and accessibility for analytics.
  • Design and maintain cloud-native data architectures, including Data Lake, Data Warehouse, and Data Lakehouse.
  • Develop data processing solutions using Spark and PySpark in distributed environments.
  • Collaborate with cross-functional teams to translate business requirements into efficient, scalable data solutions.
  • Support analytics use cases by enabling data consumption for reporting, dashboards, and visualization tools.
  • Stay up to date with modern data platform technologies and best practices, contributing to continuous improvement of the data ecosystem.

Kenntnisse

Python
SQL
Agile
English (B2+)

Tools

Apache Spark
PySpark
AWS
S3
Glue
Athena
QuickSight
Azure
Data Factory
Databricks
ADLS
Data Lake
Delta Lake
Data Warehouse
Scrum
Kanban

Jobbeschreibung

Описание:

Vention is a global engineering partner to tech leaders and fast-growing startups, combining product development expertise with an AI-first approach and reliable delivery. Its data engineering teams help startups and enterprises turn data into smart decisions and better user experiences by building scalable data pipelines and infrastructure.

Задачи:
  • Build, optimize, and maintain scalable ETL/ELT pipelines using cloud platforms;
  • Integrate and manage diverse data sources, ensuring data quality, reliability, and accessibility for analytics and ML use cases;
  • Design and maintain cloud-native data architectures, including Data Lake, Data Warehouse, and Data Lakehouse;
  • Develop data processing solutions using Spark and PySpark in distributed environments;
  • Collaborate with cross-functional teams to translate business requirements into efficient, scalable data solutions;
  • Support analytics use cases by enabling data consumption for reporting, dashboards, and visualization tools;
  • Stay up to date with modern data platform technologies and best practices, contributing to continuous improvement of the data ecosystem.
Требования:
  • 4+ Years of experience in Data Engineering, delivering end-to-end data solutions;
  • Strong hands‑on experience with Python and SQL;
  • Solid expertise with Apache Spark and PySpark for distributed data processing;
  • Hands‑on experience with AWS, including Glue, S3, Athena, and QuickSight, or Azure, including Data Factory, Databricks, and ADLS;
  • Good understanding of modern data architectures, including Data Lake, Delta Lake, and Data Warehouse;
  • Experience working in an Agile environment, such as Scrum or Kanban;
  • English B2+, comfortable communicating with English‑speaking customers.
Условия:
  • Salary from 10000 PLN gross;
  • EDU corporate community with tech communities, interest clubs, events, an R&D lab, a knowledge base, and a dedicated AI track;
  • Licenses for AI tools, including GitHub Copilot and Cursor;
  • Private medical care with LuxMed;
  • Kafeteria MyBenefit credits for stores, restaurants, and cafés;
  • Access to a Multisport membership;
  • Glasses reimbursement program;
  • Referral bonuses for bringing in new talent.
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