Delivery Lead/Senior Data Engineer

Purpose of the role

To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure.

Accountabilities

• Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
• Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
• Development of processing and analysis algorithms fit for the intended data complexity and volumes.
• Collaboration with data scientist to build and deploy machine learning models.

Vice President Expectations

• To contribute or set strategy, drive requirements and make recommendations for change. Plan resources, budgets, and policies; manage and maintain policies/ processes; deliver continuous improvements and escalate breaches of policies/procedures..
• If managing a team, they define jobs and responsibilities, planning for the department's future needs and operations, counselling employees on performance and contributing to employee pay decisions/changes. They may also lead a number of specialists to influence the operations of a department, in alignment with strategic as well as tactical priorities, while balancing short and long term goals and ensuring that budgets and schedules meet corporate requirements..
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L - Listen and be authentic, E - Energise and inspire, A - Align across the enterprise, D - Develop others..

Qualifications

• Validated experience supporting enterprise data architecture strategies, defining standards and reference architectures, and balancing performance, scalability, and resilience
• Highly skilled in cloud data architecture and distributed computing paradigms, with extensive applied experience leveraging AWS data platforms such as Glue, Lambda, S3, Redshift, Athena, and Databricks.
• Advanced knowledge of data modeling techniques, including dimensional modeling, schema evolution, and design patterns for analytics, reporting, and downstream data consumption
• Demonstrated ability to define, implement, and govern data architecture standards, reference architectures, and engineering frameworks across multiple teams
• Advanced proficiency in Python, PySpark, and SQL, with the ability to guide teams on performance optimization and scalable design, rather than serving solely as a team member contributor

Skills

• Experience supporting DevOps and CI/CD strategies for data platforms using tools such as Jenkins and GitLab, embedding quality, automation, and reliability into delivery pipelines
• Ample knowledge of data governance, metadata management, data quality, and data mesh concepts, with the ability to influence enterprise, wide adoption
• Experience supporting or enabling machine learning and AI workloads, including model training, inference, or feature pipelines in partnership with Data Science or AI teams
• Considerable understanding of cloud security, IAM, data access controls, and platform governance, with experience implementing fine grained data security using tools such as Immuta
• Strategic understanding of DBT, Data Build Tool and analytics engineering practices for scalable transformation and modelling