Senior Analytics Engineer at Watu Credit Limited

We are looking for a proactive technical lead who can stabilize and develop core systems while simultaneously engineering automated AI and ML solutions. If you have mastery in Advanced SQL and Python, and a proven track record of moving models from a notebook into a reliable production environment, you will play a defining role in our technical legacy.

Key Responsibilities:

• Lead Data Architecture & Pipelines: Architect, deploy, and oversee robust end-to-end ingestion frameworks. You will ensure raw data from diverse sources is reliably integrated into our Data Warehouse using cloud-native tools (Datastream, Fabric, Spark).
• Advanced Data Transformation: Own the modeling layer by designing complex, performant transformation logic using dbt and SQL. You will establish the standards for how raw data is turned into clean, version-controlled, and analyst-ready datasets.
• MLOps & Model Productionalization: Bridge the gap between research and production by engineering the pipelines required to deploy Machine Learning models and AI agents. You will transition models from notebooks into stable, automated, and monitorable production workflows.
• Data Quality & Technical Governance: Act as the primary guardian of data integrity. You will define and enforce strict security protocols, data quality tests, and governance rules to ensure consistency and compliance across the global organization.
• Warehouse & Infrastructure Excellence: Manage the administration of the Data Warehouse (BigQuery/Fabric), optimizing for performance, cost-efficiency, and scalability. You will select and maintain the Analytics Engineering tooling that empowers the broader data team.
• Intelligent Automation: Lead the development of internal AI tools and "intelligent workflows." You will leverage our data foundations to build custom agents and automated processes that solve complex business logic challenges.
• Technical Leadership & Mentorship: Define the "gold standard" for engineering practices (CI/CD, documentation, modularity) within the department, providing guidance to other team members and assisting in the long-term evolution of our data strategy.

Requirements

Knowledge, Skills, and Experience

• Experience: At least 5 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 (specifically for data manipulation, automation, and API integrations).
• Data Architecture: Deep expertise in modern Data Warehouse design (BigQuery, Microsoft Fabric), dimensional modeling, and implementing scalable architectural patterns.
• Modern Data Stack: Extensive hands-on experience with the ELT/ETL lifecycle, including advanced transformation workflows using dbt (Data Build Tool) and orchestration.
• Cloud Infrastructure: Strong proficiency with Google Cloud Platform (GCP)—specifically BigQuery, Datastream, and Dataflow—and experience with Spark for large-scale data processing.
• Software Excellence: A strong proponent of engineering best practices, including Git version control, CI/CD pipelines, and writing clean, maintainable, and well-tested code.