Data Engineer

Information Technology (IT)
Mbarara
April 17, 2025
Jobs

Job Description

About the Organization

Raising The Village exists because we believe that together we can find straightforward solutions to complex problems of inequality. Together, we can achieve what is impossible alone. Our story is borne as a result of two deep convictions: ultra poverty is the worst form of inequality in our world; we have the opportunity to end ultra poverty in our generation.

Since our inception, we have focused our work on partnering with last-mile, rural communities in Uganda to develop initiatives that pave the pathway out of ultra-poverty towards economic self-sufficiency. We believe that everyone deserves an opportunity to make choices and have a real chance at life. Through our partnerships we resource, guide, train, and equip ultra-poor families to make empowering decisions, access new opportunities, and shape their future.

Our work and success is the result of cross cultural collaboration between our staff and village partners, the local and federal government of Uganda, and experts from around the globe all working together. Fuelled by the support of our donors, we cumulatively reached 1 million people living in ultra-poverty in 2024.

Job Summary

The Data Engineer will play a crucial role in the VENN department by designing, building, and maintaining scalable data pipelines, ensuring efficient data ingestion, storage, transformation, and retrieval. The role involves working with large-scale structured and unstructured data, optimizing workflows, and supporting analytics and decision-making. 

The ideal candidate will have deep expertise in data pipeline orchestration, data modeling, data warehousing, and batch/stream processing. They will work closely with cross-functional teams to ensure data quality, governance, and security while enabling advanced analytics and AI-driven insights to support Raising The Village’s mission to eradicate ultra-poverty. 

Key Duties and Responsibilities

Data Pipeline Development & Orchestration

  • Design, develop, and maintain scalable ETL/ELT pipelines for efficient data movement and transformation.
  • Develop and maintain workflow orchestration for automated data ingestion and transformation.
  • Implement real-time and batch data processing solutions using appropriate frameworks and technologies.
  • Monitor, troubleshoot, and optimize pipelines for performance and reliability.

Data Architecture & Storage

  • Build and optimize data architectures, warehouses, and lakes to support analytics and reporting.
    • Work with both cloud and on-prem environments to leverage appropriate storage and compute resources.
  • Implement and maintain scalable and flexible data models that support business needs.

Data Quality, Security, & Governance

  • Ensure data integrity, quality, security, and compliance with internal standards and industry best practices.
  • Support data governance activities, including metadata management and documentation, to enhance usability and discoverability.
  • Collaborate on data access policies and enforcement across the organization.

Cross-functional Collaboration & Solutioning

  • Work closely with cross-functional teams (analytics, product, programs) to understand data needs and translate them into technical solutions.
  • Support analytics and AI teams by providing clean, accessible, and well-structured data.

Innovation & Continuous Improvement

  • Research emerging tools, frameworks, and data technologies that align with RTV’s innovation goals.
  • Contribute to DevOps workflows, including CI/CD pipeline management for data infrastructure.

Educational Qualifications, Skills, and Experience Required

  • Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. (Master’s is a plus!)
  • Experience: 4+ years of hands-on work in data engineering and building data pipelines.
  • Programming: Strong in SQL and Python—you can clean, process, and move data like a pro.
  • Data Tools: Experience using workflow tools like Airflow, Prefect, or Kestra.
  • Data Transformation: Comfortable working with tools like DBT, Dataform, or similar.
  • Data Systems: Hands-on with data lakes and data warehouses—you’ve worked with tools like BigQuery, Snowflake, Redshift, or S3.
  • APIs: Able to build and work with APIs (e.g., REST, GraphQL) to share and access data.
  • Processing: Know your way around batch processing tools like Apache Spark and real-time tools like Kafka or Flink.
  • Data Design: Good understanding of data modeling, organization, and indexing to keep things fast and efficient.
  • Databases: Familiar with both relational (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB) databases.
  • Cloud: Experience with major cloud platforms like AWS, Google Cloud, or Azure.
  • DevOps: Know your way around Docker, Terraform, Git, and CI/CD tools for smooth deployments and testing.
  • Strong ability to design, implement, and optimize scalable data pipelines.
  • Experience with data governance, security, and privacy best practices.
  • Ability to work collaboratively and engage with diverse stakeholders.
  • Strong problem-solving and troubleshooting skills.
  • Ability to effectively manage conflicting priorities in a fast-paced environment.
  • Strong documentation skills for technical reports and process documentation.
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