Principal Data Engineer
2024-09-27
Madrid
Role Overview:You will be leading the data engineering strategy and delivery across global projects and products. You will be responsible for complex technical problems and identifying solutions with minimal guidance and support. Your role is to deliver technical authority, with influence across multiple squads, disciplines, and projects. You will closely work with domain engineering managers to implement the solutions for data products following the best technical practices.What you do - Things you need for this role:
Software Engineering – 6+ years of professional software development experience in data engineering.
Demonstrable experience working with Databricks, PySpark, Python, SQL, Delta Lake
Data Quality – Spearhead initiatives to elevate data quality across all data products, implementing robust validation processes, data cleansing methodologies, and quality assurance frameworks to ensure the delivery of accurate, consistent, and reliable data assets throughout the organization.
Demonstrable experience in designing scalable cloud data architectures (preferrable Azure), particularly with data lake technology and event driven architecture.
Technology - Drive the strategic selection and implementation of cutting-edge data engineering technologies, frameworks, and methodologies. Evaluate and recommend appropriate tools and languages to optimize data processing, storage, and analysis. Guide the data community in adopting industry best practices for internal tool development.
Performance Optimization: Extensive and evidenced experience in designing and implementing high-performance data pipelines, optimizing query execution, and fine-tuning data systems to ensure scalability and efficiency in large-scale data environments.
Mentorship and Team Development: Proven track record of nurturing talent, providing technical mentorship, and fostering a culture of continuous learning and innovation within data engineering teams.
Demonstrated ability to bridge the gap between technical solutions and business needs, translating complex data concepts into actionable insights and aligning data engineering initiatives with key business objectives and strategies
Exceptional ability to collaborate across diverse teams, manage expectations, and effectively communicate complex technical concepts to both technical and non-technical stakeholders, ensuring alignment and buy-in for data engineering initiatives.
Software Engineering – 6+ years of professional software development experience in data engineering.
Demonstrable experience working with Databricks, PySpark, Python, SQL, Delta Lake
Data Quality – Spearhead initiatives to elevate data quality across all data products, implementing robust validation processes, data cleansing methodologies, and quality assurance frameworks to ensure the delivery of accurate, consistent, and reliable data assets throughout the organization.
Demonstrable experience in designing scalable cloud data architectures (preferrable Azure), particularly with data lake technology and event driven architecture.
Technology - Drive the strategic selection and implementation of cutting-edge data engineering technologies, frameworks, and methodologies. Evaluate and recommend appropriate tools and languages to optimize data processing, storage, and analysis. Guide the data community in adopting industry best practices for internal tool development.
Performance Optimization: Extensive and evidenced experience in designing and implementing high-performance data pipelines, optimizing query execution, and fine-tuning data systems to ensure scalability and efficiency in large-scale data environments.
Mentorship and Team Development: Proven track record of nurturing talent, providing technical mentorship, and fostering a culture of continuous learning and innovation within data engineering teams.
Demonstrated ability to bridge the gap between technical solutions and business needs, translating complex data concepts into actionable insights and aligning data engineering initiatives with key business objectives and strategies
Exceptional ability to collaborate across diverse teams, manage expectations, and effectively communicate complex technical concepts to both technical and non-technical stakeholders, ensuring alignment and buy-in for data engineering initiatives.