• Lead the design, development, and optimization of ETL processes to ensure efficient data extraction, transformation, and loading. Ensure data integrity, quality, and consistency during ETL processes. Troubleshoot and resolve complex ETL issues.
• Implement performance tuning and optimization techniques for ETL processes. Develop and implement comprehensive data quality checks and validation procedures. Monitor and report on data quality metrics, identifying and addressing anomalies.
• Lead the creation and optimization of data models to support business needs and analytics. Ensure data models are scalable, efficient, and well-documented. Collaborate with data scientists and analysts to understand and meet data modeling requirements.
• Establish and enforce data modeling standards and best practices. Lead the development and implementation of data engineering standards and best practices. Ensure alignment of data engineering activities with organizational goals and strategies.
• Foster a culture of technical excellence and continuous improvement.
• Effectively communicate with Data Product Managers to align data engineering requirements and priorities, ensuring data solutions meet business needs.
• Collaborate closely with junior data engineers, providing mentorship, technical guidance, and support to enhance their skills and ensure adherence to best practices.
• Work with Data Scientists to understand analytical requirements, translating them into scalable and efficient data models and pipelines that support advanced analytics and machine learning.
• Coordinate with the Data Platform Manager to ensure alignment on cloud strategies, platform optimization, and integration standards, facilitating seamless operation and performance of data solutions.
• Maintain open lines of communication with cross-functional teams, providing regular updates on project progress, addressing challenges, and incorporating feedback to continuously improve data engineering processes and outcomes.

Benefits and Perks to Help You Keep Climbing

The Senior Data Engineering at DTH Virgin Atlantic team leads the design, development, and optimization of ETL processes, ensuring data integrity, quality, and efficiency while mentoring junior team members on best practices.

They develop and implement comprehensive data quality checks, monitor data quality metrics, and lead initiatives to improve data quality, providing coaching to junior analysts.

The role involves leading the creation and optimization of scalable and efficient data models, collaborating with data scientists and analysts, and establishing data modeling standards.

They provide technical leadership, develop and enforce data engineering standards, and offer guidance and oversight on data engineering projects, fostering a culture of technical excellence and continuous improvement.

Collaborating with cross-functional teams and stakeholders, they communicate project updates, gather feedback to improve processes, and lead initiatives to drive data engineering excellence, building strong relationships with partners and collaborators.

What you need to succeed (minimum qualifications)

• Bachelor’s degree in Computer Science, Data Science, Information Technology, or a related field, or equivalent practical experience.
• 4-7 years of experience in data engineering, with a strong focus on ETL processes, data quality, and data modeling.
• Proficiency in SQL, Python, and data engineering tools, with hands-on experience in Azure technologies such as Azure Data Factory, Azure Databricks, and Azure SQL Database.
• Demonstrated ability to lead the design, development, and optimization of data pipelines and models, ensuring high performance and scalability.
• Strong knowledge of data quality assurance practices, including the development and implementation of validation checks and monitoring systems.
• Experience in developing and enforcing data engineering standards and best practices, with a track record of providing technical leadership and mentoring junior team members. Strogn CICD and DevOps experience as well as an understanding or experience of ML and AI Ops.
• Excellent collaboration and communication skills, with the ability to work effectively with cross-functional teams and stakeholders to deliver data engineering solutions.
• Proactive approach to continuous improvement and innovation, with a commitment to staying updated with industry trends and advancements in data engineering technologies.

Behavioral Competencies:

• Ability to produce high quality results, work in a collaborative environment by embracing diverse perspectives and with a solution-based approach.