Senior Data Engineer

HeadHR; Kraków; On-site; PLN 180,000 - 240,000; Full time

Job summary

HeadHR is seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines, models, and robust data solutions. You will work to ensure data quality, governance, and compliance across the Data Foundation ecosystem, driving efficiency and long-term usability.

The role requires 5+ years in data engineering, strong Python, DBT, Airflow, PySpark, and AWS skills, with a focus on scalable architectures and cross-functional collaboration within a gaming-oriented data

Qualifications

• Minimum of 5 years commercial work experience in data engineering or a related field.
• Bachelor’s or higher degree in Computer Science, Software Engineering, or a related field.
• Proficiency in Python, essential for data processing and analysis tasks.
• Hands-on experience with DBT and Airflow.
• Commercial experience using PySpark and Databricks.
• Proficiency in AWS cloud infrastructure (AWS).
• Effective communication and teamwork skills.

Responsibilities

• Design, build, and optimize scalable pipelines for data, supporting insight into the Data Foundation ecosystem
• Design and implement data models using industry best practices that capture a complete ecosystem view of internal customer experiences, while ensuring accuracy, scalability, and long-term usability
• Architect and implement robust, maintainable, and high-performance data solutions
• Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for content, growth, and pubsports areas of coverage
• Optimize query performance and resolve pipeline bottlenecks to improve data accessibility
• Evaluate and adopt new tools, frameworks, and methodologies to advance data engineering capabilities
• Support cost optimization by ensuring scalable and efficient data solutions
• Ensure data quality, governance, and compliance with regulatory standards (e.g., GDPR, CCPA)
• Contribute to engineering discipline by shaping infrastructure, craft standards, tooling, and organizational best practices

Skills

Python

DBT

Airflow

PySpark

Databricks

AWS

Communication

Education

Bachelor's or higher in CS/SE

Tools

DBT

Airflow

PySpark

Databricks

Job description

Responsibilities:

• Design, build, and optimize scalable pipelines for data, supporting insight into the the Data Foundation ecosystem


• Design and implement data models using industry best practices that capture a complete ecosystem view of internal customer experiences, while ensuring accuracy, scalability,and long-term usability


• Architect and implement robust, maintainable, and high-performance data solutions


• Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for content, growth, and pubsports areas of coverage


• Optimize query performance and resolve pipeline bottlenecks to improve data accessibility


• Evaluate and adopt new tools, frameworks, and methodologies to advance data engineering capabilities


• Support cost optimization by ensuring scalable and efficient data solutions


• Ensure data quality, governance, and compliance with regulatory standards (e.g., GDPR, CCPA)


• Contribute to engineering discipline by shaping infrastructure, craft standards, tooling, and organizational best practices

Kogo poszukujemy?

Required Qualifications:

• Minimum of 5 years commercial work experience in data engineering or a related field


• Bachelor’s or higher degree in Computer Science, Software Engineering, or a related field


• Proficiency inPython, essential for data processing and analysis tasks


• Hands-on experience withDBT andAirflow


• Commercial experience usingPySparkandDatabricks


• Proficiency inAWScloud infrastructure (AWS)


• Effective communication and teamwork skills

Nice to Have:

• Experience withML OperationsandGenAIpipelines and infrastructure


• Experience in thegaming industry, particularly with online multiplayer games


• Experience working with cross-discipline organizations that build data products


• Proficient in large-scale data manipulation across various data types


• Demonstrated ability to troubleshoot and optimize complex ETL pipelines


• Proven experience mentoring and guiding other engineers