Key Responsibilities

• Build & Deploy Pipelines: Design, create, and deploy robust data ingestion pipelines to continuously feed the Data Warehouse with raw data from diverse sources using cloud-hosted ingestion tools.
• Data Transformation: Create and maintain efficient processes to transform raw data into clean, organized, and analyst-ready datasets using dbt, SQL, and Google Cloud tools (Dataflow, Datastream).
• Data Quality & Security: act as the guardian of the Analytics data, taking full responsibility for data quality, consistency, and the implementation of strict security protocols.
• Governance Implementation: Define and implement data governance rules to ensure data integrity and compliance across the organization.
• Warehouse Administration: Manage the administration of the Data Warehouse, ensuring optimal performance, organization, and accessibility.
• Tooling & Infrastructure: Implement, develop, and maintain the necessary Analytics Engineering tools and infrastructure to support the wider data team.
• Architecture Support: Actively assist in defining and evolving the data architecture to ensure it remains scalable and efficient as the business grows.
• AI Development & Automation: Work on the company’s initial AI initiatives by developing custom agents, tools, and intelligent workflows that leverage our data foundation to automate complex business processes.

Requirements:

Knowledge, Skills, and Experience

• Experience: At least 3 years of proven experience working in a Data Engineering or Back-end Engineering role.
• Core Languages: Advanced proficiency in SQL and strong coding skills in Python.
• Data Warehousing: A deep understanding of modern Data Warehouse technologies, architectural patterns, and industry best practices.
• Data Operations: Expertise with the latest tools and processes for data ingestion, transformation, and management (ETL/ELT).