Responsibilities

• Take ownership of and optimise existing data pipelines, ensuring efficiency, reliability, and scalability
• Manage and guide 1-2 mid/Senior Engineers in the Data team, setting the team direction and giving support and guidance to help enhance their own abilities and careers
• Design, implement and maintain new data models, ETL processes, and data integrations
• Develop and maintain data visualisations using Tableau, making complex data easily understandable for stakeholders across the organisation
• Collaborate with team mates across different functions to understand their data needs and develop solutions to help them solve problems more effectively
• Ensure data quality, integrity, and security across all data systems
• Implement best practices for data governance, including data cataloging, metadata management, and data provenance tracking
• Stay current with emerging technologies and methodologies in the data engineering field, and propose innovations to improve our data infrastructure
• Work closely with the engineering team to produce insights from
• Own the documentation and knowledge sharing around data processes, insights and ownership

Requirements

• Extensive experience in data engineering, with a proven track record of building and maintaining large-scale data systems
• Strong proficiency with AWS Redshift and other cloud-based data warehouse solutions
• Expert-level SQL skills and experience with data modeling techniques
• Proficiency in data visualization tools, particularly Tableau
• Experience with ETL tools and processes
• Knowledge of data governance principles and best practices
• Strong communication skills, with the ability to explain complex data concepts to non-technical stakeholders
• A passion for continuous learning and staying up-to-date with the latest trends in data engineering and analytics

Bonus Skills

• Experience with cloud providers such as AWS and Azure, particularly their data services
• Familiarity with event-driven architectures and how they impact data pipelines
• Familiarity with data streaming technologies such as Apache Kafka or AWS Kinesis
• Experience implementing and assessing process or technical improvements for data monitoring, testing, and release management
• Knowledge of machine learning and AI technologies and their application in data engineering