Position Overview 

We are seeking a strategic and detail-oriented Analytics Engineer to support the setup and maintenance of our data governance and analytics infrastructure. As we grow our impact across East Africa, having reliable, well-structured data is crucial for measuring and improving our programs. This role will be fundamental in building the foundation for how we collect, transform, and utilize data across the organization.

Our organization is at an exciting inflection point in our data journey. We've built strong monitoring and evaluation systems, and now we're ready to take our data capabilities to the next level. We need someone who can bridge the gap between our analytics and technology teams, establishing standardized practices that will scale with our growth. The Analytics Engineer will be instrumental in creating consistent metric definitions, implementing robust data quality frameworks, and building efficient data transformations that enable reliable impact measurement.

We pride ourselves on being data-driven in our decision-making, and this role will be at the heart of making that possible. You'll work closely with our M&E and technology teams to establish data standards, implement quality controls, and create scalable analytics infrastructure. We're looking for someone who is excited about the challenge of building these systems from the ground up, someone who can think strategically about data architecture while also rolling up their sleeves to implement solutions.

This is a unique opportunity to shape how an organization uses data to drive impact. You'll have the freedom to establish best practices, implement modern data tools like dbt, and create processes that will scale with our growth. If you're passionate about using data to drive social impact and excited about building robust data systems, we want to hear from you.

Examples of expected projects include:

• Core Metrics Standardization: You'll lead the standardization of our key metrics by reviewing current calculations, establishing standard definitions, and implementing these through central mart models with appropriate testing and documentation.
• Data Quality Monitoring Framework: You'll build a comprehensive quality monitoring system featuring dbt tests, custom checks, and automated alerts to ensure data reliability and consistency across our systems.
• Naming Convention Implementation: You'll develop and implement standardized naming conventions across our data infrastructure, including creating new standards, planning the migration, and setting up automated compliance check

Primary Collaborations:

• M&E Team: Partner on data quality standards, monitoring requirements, indicators, and metadata standards
• Tech Team: Work with developers and product owners on data collection tools, database schemas, and technical requirements

Performance Objectives 

1. Data Modeling & Transformations: (50%)

• Design and implement dbt models including base, intermediate, and mart layers
• Create dimension tables following data warehousing principles
• Implement business logic in transformations
• Set up incremental processing for data models

2. Data Quality Framework: (30%)

• Embed quality tests in dbt models (uniqueness, not-null, relationships)
• Set up data freshness checks
• Create custom data quality tests for business rules
• Monitor data quality metrics
• Establish and maintain alerting systems for quality issues

3. Data Governance & Standards: (10%)

• Develop and enforce naming conventions across databases and tools
• Create field-level standards (date formats, currency handling, etc.)
• Standardize calculations for key metrics
• Build and maintain data dictionaries
• Document business rules and assumptions

4. Process & Documentation: (10 %)

• Create PR templates for model changes
• Set up model documentation requirements
• Build style guides for SQL/dbt development
• Establish testing requirements for new models
• Create technical documentation

Qualifications 

Required Skills:

• Strong SQL expertise
• Experience with dbt
• Understanding of git management
• Understanding of data modeling principles
• Experience with data quality implementation
• Strong documentation skills
• Ability to collaborate with technical and non-technical stakeholders

Preferred Skills:

• Experience with NGO/M&E data
• Knowledge of data collection tools (e.g. SurveyCTO, ODK, Textit)
• Understanding of impact measurement
• Experience with data governance implementation
• List out the qualifications here, without being too repetitive
• Fits our Five Culture Tenets (see What is Educate! About? below); Learn more by looking at Educate!’s culture deck here