Senior Data Engineer
⚲ Gliwice
21 840 - 26 880 PLN netto (B2B)
Wymagania
- Data
- SQL
- PySpark
Opis stanowiska
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Join an international media and broadcasting organization as a Senior Data Engineer. You will design, optimize, and maintain enterprise-grade cloud data platforms, building robust ETL/ELT pipelines capable of processing massive data volumes. This role demands a strong focus on Data Quality and the engineering rigor required to manage complex data ecosystems spanning both AWS and Google Cloud environments.
Details
Start Date: ASAP
Duration: Long-term
Working Hours: preferably 9:00–17:00
Rate: 130–160 PLN/hourResponsibilities
• Pipeline Engineering: Build, optimize, and maintain scalable cloud-native data pipelines (ETL/ELT) using PySpark and SQL.
• Cloud & Platform Infrastructure: Develop and maintain data solutions across distributed cloud environments (AWS, GCP, and Databricks).
• Data Quality & Testing: Ensure high-fidelity data processing with a strict focus on Data Quality, backed by comprehensive unit and integration testing.
• Automation & CI/CD: Automate deployment pipelines and manage infrastructure as code.
• Cross-functional Collaboration: Partner with both technical and business stakeholders in an international, distributed team setup.
Requirements
• Core Languages: Deep, senior-level mastery of PySpark and SQL.
•
Cloud & Orchestration Ecosystem:
• AWS: AWS CDK, AWS Lambda.
• GCP: Google Cloud Platform, Google App Engine, BigQuery, Dataproc.
• Data Platforms: Databricks, Airflow.
• Infrastructure as Code: Terraform.
• CI/CD Tools: GitHub Actions, Jenkins.
• Quality Assurance: Proven experience in Data Quality frameworks alongside Unit and Integration Testing methodologies.
• Soft Skills & Languages: Exceptional communication skills for international project delivery; English minimum B2+ (for daily communication).
Join an international media and broadcasting organization as a Senior Data Engineer. You will design, optimize, and maintain enterprise-grade cloud data platforms, building robust ETL/ELT pipelines capable of processing massive data volumes. This role demands a strong focus on Data Quality and the engineering rigor required to manage complex data ecosystems spanning both AWS and Google Cloud environments.
Details
Start Date: ASAP
Duration: Long-term
Working Hours: preferably 9:00–17:00
Rate: 130–160 PLN/hourResponsibilities
• Pipeline Engineering: Build, optimize, and maintain scalable cloud-native data pipelines (ETL/ELT) using PySpark and SQL.
• Cloud & Platform Infrastructure: Develop and maintain data solutions across distributed cloud environments (AWS, GCP, and Databricks).
• Data Quality & Testing: Ensure high-fidelity data processing with a strict focus on Data Quality, backed by comprehensive unit and integration testing.
• Automation & CI/CD: Automate deployment pipelines and manage infrastructure as code.
• Cross-functional Collaboration: Partner with both technical and business stakeholders in an international, distributed team setup.
Requirements
• Core Languages: Deep, senior-level mastery of PySpark and SQL.
•
Cloud & Orchestration Ecosystem:
• AWS: AWS CDK, AWS Lambda.
• GCP: Google Cloud Platform, Google App Engine, BigQuery, Dataproc.
• Data Platforms: Databricks, Airflow.
• Infrastructure as Code: Terraform.
• CI/CD Tools: GitHub Actions, Jenkins.
• Quality Assurance: Proven experience in Data Quality frameworks alongside Unit and Integration Testing methodologies.
• Soft Skills & Languages: Exceptional communication skills for international project delivery; English minimum B2+ (for daily communication).
🔍 Dekoder Ogłoszenia
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enterprise-grade cloud data platforms
Oznacza, że platformy danych mają spełniać wysokie standardy jakości i niezawodności, ale może też sugerować złożoność i potencjalnie długi czas wdrożenia.
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massive data volumes
Wskazuje na potrzebę przetwarzania dużych ilości danych, co może oznaczać wyzwania związane z wydajnością i skalowalnością.
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engineering rigor
Podkreśla potrzebę stosowania dobrych praktyk inżynierskich, ale może też sugerować bardzo formalne i czasochłonne procesy.
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complex data ecosystems
Sugeruje, że systemy danych są rozbudowane i mogą być trudne do zrozumienia oraz zarządzania.
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international, distributed team setup
Oznacza pracę z ludźmi z różnych stref czasowych, co może wymagać elastyczności w godzinach pracy i dobrej komunikacji.