Analytics Engineer at Watu Credit Limited

Watu View all jobs

  • Kenya
  • Permanent
  • Full-time
  • 1 month ago
Watu Credit Limited is a dynamic and fast-growing non-bank finance company. Watu Credit Limited harnesses technology to offer unsecured lending, primarily via mobile services. We aim to become the leading African provider of a broad set of inclusive financial products, delivered through technology in a fast, efficient and professional manner.Analytics EngineerKey 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).
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