Data Engineering Lead

Job Description

Responsibility

• Lead the strategy, design, and implementation of scalable, high-performance data pipelines, platforms, and architectures.
• Manage the Data Engineering team’s backlog, priorities, and capacity to ensure timely delivery of high-quality outputs.
• Collaborate with data scientists, analysts, and product teams to understand data requirements and deliver fit-for-purpose solutions.
• Oversee and contribute to the development of ETL/ELT processes, data models, and data integration frameworks.
• Ensure data quality, consistency, and governance across all systems and datasets.
• Monitor and optimise data infrastructure for performance, scalability, and cost efficiency.
• Define and enforce engineering best practices, coding standards, and security guidelines.
• Evaluate and adopt new technologies, tools, and frameworks that can improve data engineering capabilities.
• Provide technical leadership and mentorship to data engineers, fostering a high-performance culture.
• Troubleshoot complex data issues and drive root cause analysis and remediation.
• Partner with stakeholders to ensure data platforms align with business needs and strategic objectives.
• Maintain comprehensive documentation of data processes, architecture, and integrations.
• Ensure compliance with relevant data protection regulations and industry standards.
  
  

About You

Hard criteria

• You are located in Albania, Bosnia and Herzegovina, Bulgaria, Greece, Hungary, Kosovo, North Macedonia, Poland, Romania, Serbia, South Africa, or Tunisia
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• Minimum 7 years’ experience in data engineering.
• At least 2 years of leadership experience managing or mentoring engineers
• Proven experience in designing, building, and maintaining scalable ETL/ELT pipelines and data platforms.
• Proficiency in SQL, Python, and data modelling techniques.
• Experience with cloud-based data architectures (e.g., Azure, AWS, or GCP).
• Strong understanding of data governance, quality frameworks, and security best practices.
• Experience integrating data from multiple sources, including APIs and third-party systems.
• Familiarity with modern data tools and technologies (e.g., Databricks, Snowflake, Apache Spark, Airflow, Microsoft Fabric).

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