Data Engineer At Watu Credit Limited

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Analytics Engineer

Job Summary

As the business continues to grow rapidly, the Analytics Engineer will join the Data Analytics department and play an important role in establishing and strengthening the organization’s data engineering foundations. The position will contribute to defining best practices, pipeline architecture, and technical standards while building reliable and scalable data solutions.

The role will involve working with technologies including Google Cloud Platform, Datastream, Microsoft Fabric, Databricks, and dbt. In addition to developing the organization’s core data infrastructure, the successful candidate will have the opportunity to support the company’s initial AI initiatives by developing internal AI tools, agents, and intelligent workflows.

The primary focus will be on designing and deploying reliable data ingestion pipelines, administering the Data Warehouse, and developing processes that convert raw information into clean, structured, secure, and analyst-ready data. As these systems are established and stabilized, the role will also support the development and implementation of AI capabilities.

Strong knowledge of advanced SQL, Python for data engineering and automation, modern Data Warehouse practices, and data ingestion and transformation tools is required. Experience with AI development is an advantage.

Key Responsibilities

Build and Deploy Data Pipelines

• Design, develop, and deploy reliable data ingestion pipelines that continuously supply the Data Warehouse with raw data from different sources.

• Use cloud-based ingestion tools to support the movement and availability of data within the organization.

Data Transformation

• Develop and maintain efficient processes for transforming raw data into clean, structured, and analyst-ready datasets.

• Use dbt, SQL, and Google Cloud tools such as Dataflow and Datastream to support data transformation processes.

Data Quality and Security

• Take responsibility for the quality, consistency, and reliability of Analytics data.

• Implement and maintain appropriate security protocols to protect organizational data.

• Ensure that data remains accurate and dependable for analytics and other business applications.

Data Governance

• Define and implement data governance rules across the organization.

• Support data integrity and compliance through appropriate governance processes and standards.

Data Warehouse Administration

• Manage the administration of the Data Warehouse.

• Monitor and support optimal performance, organization, and accessibility of the warehouse.

Tooling and Infrastructure

• Implement, develop, and maintain Analytics Engineering tools and infrastructure required to support the wider data team.

• Help ensure that the team has the necessary systems and tools to manage data effectively.

Architecture Support

• Contribute to the development and evolution of the organization’s data architecture.

• Help ensure the architecture remains scalable, efficient, and suitable for the company’s continued growth.

AI Development and Automation

• Contribute to the company’s initial AI initiatives.

• Develop custom AI agents, tools, and intelligent workflows.

• Leverage the organization’s data foundation to automate complex business processes.

Requirements

Knowledge, Skills, and Experience

• At least 3 years of proven experience working in Data Engineering or Back-end Engineering.

• Advanced proficiency in SQL.

• Strong programming skills in Python.

• Deep understanding of modern Data Warehouse technologies, architectural patterns, and industry best practices.

• Strong knowledge of data ingestion, transformation, and management processes, including ETL and ELT.

Additional Technical Knowledge

The following technical skills and experience would be an advantage:

• Hands-on experience with Google Cloud solutions, particularly Cloud Storage, BigQuery, Datastream, and Dataflow.

• Practical experience using dbt (data build tool).

• Familiarity with modern AI engineering concepts, including Vector Databases, Retrieval-Augmented Generation (RAG), context management, and connecting LLMs with tools and external APIs.

• Experience with non-relational databases or Big Data technologies.

• Knowledge of data streaming analytics and real-time data processing.

• Proficiency with Git and version control.

• Knowledge of programming languages other than Python and SQL.

Non-Technical and Soft Skills

• Strong attention to detail and a rigorous approach to data quality and accuracy.

• Ability to work independently and proactively while anticipating potential problems.

• Self-motivated and target-oriented, with the ability to manage a personal roadmap and meet delivery objectives.

• Strong teamwork and communication skills.

• Ability to connect technical requirements and complexity with business needs.

What We Offer

• Opportunity to work as part of an international, dynamic, and driven team with ambitious goals.

• Opportunities to learn, develop, and grow with the organization.

• Competitive compensation package.

• Health benefits.

Application

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