Senior Data Engineer

MARGO

. Implement scalable, reliable and reusable data pipelines using Azure Data Factory and Apache Airflow .

Posted 11/11/2026full-timeRemote • PolandSenior💰 PLN 180 - PLN 200 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in data engineering with a strong focus on implementing scalable data pipelines using Azure Data Factory and Apache Airflow. Proficient in developing data transformations with PySpark and SQL, while ensuring operational readiness and optimizing performance across data workloads.

Highest-signal resume keywords

Data Engineering ExperiencePython (PySpark) ExpertiseAdvanced SQL SkillsAzure Data Factory ProficiencyApache Airflow Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline ImplementationData Transformation DevelopmentPerformance OptimizationData Modelling FundamentalsETL/ELT Design PatternsBatch and Streaming PipelinesMedallion Architecture KnowledgeMetadata-Driven ArchitectureData Warehouse ArchitectureData Lakehouse Architecture

Soft Skills

CollaborationProblem-SolvingTechnical Decision-MakingRisk DetectionDocumentation

Tools & Technologies

Azure DatabricksUnity CatalogPower BITableauAzure Data Platform

Industry Keywords

Data ProductsIncident ManagementRoot Cause AnalysisOperational ProcessesRunbooks

Tech Stack

Tools & technologies

AirflowApacheAzureETLPySparkPythonSQLTableauUnity

About the role

Key responsibilities & impact

Implement scalable, reliable and reusable data pipelines using Azure Data Factory and Apache Airflow
Develop data transformations in Azure Databricks using PySpark and SQL
Create and extend data products following Bronze, Silver, and Gold Medallion Architecture layers
Optimize performance, cost, and reliability of data workloads
Raise important questions during business maturation to detect risks before development
Participate in architecture reviews and technical decision-making
Promote engineering excellence through code reviews and design reviews
Collaborate with Architects and Product Owners in data product technical maturation
Support the Run/Operations team for production pipelines and data products
Troubleshoot and resolve data incidents, failures, and performance issues
Participate in incident management, root cause analysis, and post-mortems
Ensure proper handover, documentation, and operational readiness of developed solutions
Contribute to improving runbooks, monitoring, and operational processes

Requirements

What you’ll need

6+ years of experience in data engineering
Strong technical expertise in Python (PySpark)
Advanced SQL skills
Hands-on experience with Azure-based Data Platform
Experience with Databricks
Experience with Unity Catalog
Experience with Apache Airflow
Experience with Azure Data Factory
Experience with reporting and BI tools such as Power BI, Tableau or similar
Solid understanding of data modelling fundamentals, main architectures and design patterns
Knowledge of Data Warehouse, Data Lake and Data Lakehouse architectures
Knowledge of Medallion architecture
Knowledge of metadata-driven architecture
Knowledge of ETL/ELT design patterns
Understanding of partitioning, optimization, and performance
Understanding of batch and streaming pipelines