Data Engineer

Job Description: The resource will operate as a hybrid engineer, providing support across the following domains: Software Engineering & Automation: Designing and deploying scripts and tools to automate repetitive manual tasks. This includes enhancing existing frameworks to streamline internal workflows and integration points. Data Engineering & Optimization: Developing and maintaining robust data pipelines to ensure data integrity and high-quality insights. This includes supporting data hygiene initiatives. Backlog Execution: Resolution of cross-functional technical tasks as prioritized by leadership. Autonomy: Ability to navigate complex backlogs and identify opportunities for optimization with minimal supervision. Quality: Delivery of clean, maintainable code and scalable data structures that adhere to team standards. Communication: Regular updates on progress, blockers, and the measurable impact of automation efforts. Development: Proficiency in modern programming languages (Python knowledge required) and experience with API integrations. Data: Strong SQL capabilities and experience with ETL processes, data modelling, and visualization. Systems: Familiarity with version control, CI/CD principles, and agile project management tools (e.g., Jira). Complete environment setup and knowledge transfer for both team domains. Identify and resolve initial "low-hanging fruit" backlog items. Audit current manual processes to prioritize automation opportunities. Deliver the first phase of automated workflows for repetitive tasks. Execute data pipeline enhancements to improve reporting accuracy. Contribute to the resolution of high-priority technical debt. Finalize documentation for all new automations and data workflows. Demonstrate measurable reduction in manual effort hours. Quarterly Review: Assessment of backlog velocity and strategic impact on team goals.