Job Description

The Data Engineer at RURA will design, build, and maintain scalable and reliable data pipelines using modern open-source and cloud-based technologies. This hands-on role supports evidence-based regulation by transforming raw, fragmented data into well-structured, accessible, and trusted datasets. The engineer will work closely with internal teams and external partners to understand data requirements, build efficient data workflows, and ensure secure, high-quality data integration across sectors regulated by RURA. Additionally, the role contributes to strengthening RURA’s data infrastructure by promoting best practices in data governance, automation, and platform reliability.

Responsibilities

KEY RESPONSIBILITIES

• · Design, build, and maintain secure, scalable, and efficient data pipelines to support regulatory analysis and reporting.
• · Collect, clean, transform, and integrate data from diverse sources (APIs, databases, flat files, logs) to enable downstream analysis and visualization.
• · Collaborate with internal teams and external data providers to assess requirements, define data integration methods, and troubleshoot technical issues.
• · Develop and manage data infrastructure using open-source or commercial tools, either on cloud platforms or on-premise environments.
• · Implement robust validation checks, logging, and error-handling mechanisms to ensure data quality and integrity.
• · Document technical workflows, data schemas, and pipeline logic to support transparency, collaboration, and maintainability.
• · Enforce data security and privacy standards, including encryption, access controls, and compliance with Rwanda’s data protection laws.
• · Work closely with analysts, policy teams, and departments to deliver timely, accurate, and actionable data for regulatory decision-making.
• · Optimize data workflows for performance, scalability, and cost-efficiency, especially for large or sensitive datasets.
• · Support DevOps practices in data engineering, including CI/CD pipelines, version control, automated testing, and monitoring.
• · Contribute to the development of data documentation standards, metadata management, and RURA’s data cataloging initiatives.

Requirements

Education and Experience:

• · A Masters’ degree in data engineering, computer science, software engineering, information systems, or a related technical field (e.g., data science, applied mathematics, or statistics) with a minimum of 1 year of professional experience in data engineering, ETL development, or building data infrastructure.  Or 
• · A Bachelor’s degree in Data Engineering, Computer Science, Software Engineering, Information Systems, or a related technical field (e.g., Data Science, Applied Mathematics, or Statistics), with a minimum of three (3) years of professional experience in data engineering, ETL development, database management, or building and maintaining data infrastructure.

 SKILLS AND COMPETENCIES 

• · Workflow Orchestration: Proficient in Apache Airflow to build, schedule, and monitor DAGs, including creating custom operators and managing retries and failures.
• · Containerization & Dev Environments: Skilled in using Docker to containerize applications, write Dockerfiles, and manage multi-container setups with Docker Compose.
• · Linux & Bash Scripting: Comfortable working in Linux environments for scripting, automation (e.g., cron jobs), file management, and troubleshooting.
• · Programming (Python/R): Ability to write clean, modular ETL code in Python or R, including integration with APIs, databases, and third-party services.
• · SQL & Databases: Experience writing complex queries and working with various database engines (e.g., PostgreSQL, MySQL, Clickhouse) for both OLTP and OLAP contexts.
• · Data Warehousing & Lakes: Knowledge of data modeling, partitioning, performance optimization, and handling large-scale datasets in warehousing environments.
• · Cloud Infrastructure: Capable of deploying and managing data services and infrastructure on cloud platforms such as AWS, Azure, or similar.
• · Monitoring & Logging: Experience using tools to monitor pipeline health, detect failures, and generate alerts for proactive maintenance.
• · Data Quality Management: Implement validation checks, profiling tools, and data monitoring systems to ensure consistency and reliability.
• · Version Control: Proficient in using Git for collaborative development, versioning, and deployment of data workflows.
• · Security & Privacy: Understands best practices for data protection, including access controls, encryption, and secure data transfer methods (e.g., SFTP, VPN).
• · Networking Fundamentals: Familiar with basic networking concepts like IPs, ports, DNS, and firewalls—particularly in multi-environment setups.
• · CI/CD for Data Pipelines: Experience automating deployment and testing using tools such as GitHub Actions, Jenkins, or similar frameworks.
• · Documentation & Communication: Able to document data workflows clearly and communicate technical concepts to non-technical stakeholders

CANDIDATE PROFILE

• · Possesses strong problem-solving skills and attention to detail, especially when designing and debugging data systems.
• · Works well independently and in cross-functional teams, collaborating with analysts, engineers, and policy stakeholders.
• · Demonstrates a deep understanding of data modeling, pipelines, and statistical concepts relevant to data quality and performance.
• · Comfortable sourcing, transforming, and integrating data from both structured and unstructured sources using scalable methods.
• · Able to clearly define technical problems, design solutions, and document processes for both technical and non-technical audiences.
• · Brings a proactive, creative mindset to building resilient data platforms and is motivated to improve data access and quality across the organization.
• · Proven ability to design and maintain robust data pipelines across varied data sources and formats.
• · Proficiency in both OLAP and OLTP databases such as Clickhouse, PostgreSQL, MySQL, or similar systems.
• · Comfortable working in Linux-based environments, including scripting, task automation, and basic system troubleshooting.
• · Strong programming skills in Python or another language used in data engineering (e.g., Scala, Java).
• · Familiarity with tools such as Apache Airflow, Docker, Git, and cloud platforms like AWS, GCP, or Azure.
• · Knowledge of data quality, integrity, and security standards, including best practices for governance.
• · Ability to work cross-functionally with analysts, data stewards, and policy teams to deliver high-quality data products.