Lead Data Platform

RESPONSIBILITIES

• Lead, mentor, and provide technical guidance to a stream-aligned Data Platform team spanning Data Engineering and Applied Intelligence capabilities, fostering engineering excellence and collaboration.  
• Oversee the implementation and governance of data processing, data quality, metadata, and intelligence capabilities within the product domain.  
• Design and guide scalable data pipelines, processing workflows, and reusable data capabilities aligned with platform architecture and engineering standards.  
• Establish and enforce engineering standards for code quality, peer reviews, documentation, and QA processes to ensure reliable and maintainable data capabilities. 
• Drive the creation and management of reusable data artifacts, including metadata, lineage, catalog, and documentation within the company’s data platform ecosystem.  
• Collaborate with cross-functional engineering teams to ensure data services and processing capabilities are properly integrated into product delivery and platform workflows. 
• Collaborate with product owners and business stakeholders to translate business needs into technical requirements and data-driven solutions. 
• Sync weekly with the Shared Services Pool to share learnings, discuss roadmaps, and contribute to the evolution of our central data platforms and patterns (e.g., Kafka, Airflow, ClickHouse, OpenMetadata). 
• Hands-on experience with Java and Spring Boot, particularly for integrating data services into platform applications.  
• Experience working with modern data platform ecosystems, including streaming, orchestration, and analytical platforms.  
• Exposure to advanced data analytics, machine learning, or AI capabilities, including model integration or intelligent data processing use cases.  
• Experience working in cloud environments (GCP, AWS, or Azure).  
• Knowledge of the telecommunications (Telco) domain and operational data environments. 

REQUIREMENTS 

• 3–5 years of hands-on experience in data engineering, data platform implementation, or scalable data processing environments.  
• Proven experience in a leadership or mentorship role, with a track record of guiding technical teams in delivering data-related capabilities.  
• Strong proficiency in Python and SQL for data processing, pipeline development, and data platform implementation.  Hands-on experience with modern data platform technologies, such as workflow orchestration (e.g., Airflow), event streaming (e.g., Kafka), and analytical or distributed databases (e.g., ClickHouse).  
• Good understanding of data processing lifecycle, metadata management, data quality, and governance-support capabilities.  
• Hands-on experience in metadata management, data discovery, lineage, or governance-support capabilities using platforms such as OpenMetadata or similar data catalog/governance tools. 
• Solid software engineering fundamentals, including version control (Git), CI/CD, documentation, and testing practices.  
• Strong collaboration and communication skills in working with cross-functional engineering and product teams.