Director of Performance Metrics NGO Job Educate!

Job Title: Director of Performance Metrics

Date Posted: 03/07/2026

Job Type: Full Time

Employer: Educate!

Industry: Director

Salary: Open

Location: Nairobi

Country: Kenya

Deadline: 17/07/2026

Summary:

Looking for a director job in Kenya? Educate! is recruiting a Director of Performance Metrics. The role requires strategic leadership in M&E, advanced data systems management, and data-driven learning frameworks.

Position Overview

Educate! is becoming more data- and technology-driven, and the performance metrics function is central to that shift. This function tracks our monitoring metrics—both operational metrics that help implementation teams run programs well, and classic monitoring of whether we are doing the right things: activities, outputs, and near-term outcomes. It works alongside, and hands off cleanly to, a separate evaluation team that owns deeper impact and causal work.

This is a build role at a pivotal moment, and you will drive two important shifts: First, transforming our current Performance Metrics team, structures, and processes to become AI-native. Second, you will sit on a cross-functional team of senior leaders to implement our predictive analytics strategy, working in partnership with our evaluation, product, implementation, and tech teams.

As our most senior data leader, the Director of Performance Metrics will design and own all four pillars of Educate!’s data function: data design, data engineering and infrastructure, analytics oversight, and data collection. You will operate as a “full-stack analyst” leader: technically fluent in SQL, dbt, and cloud warehousing, while acting as a people leader and a trusted cross-functional partner to Technology, Evaluation, and Product.

Expected Impact

The specific milestones below will be calibrated with the COO, but within this role’s first year, we expect:

• First 6 months: You understand the current metrics landscape, team, and data stack, and have established the function’s strategy and operating model alongside the COO. The team is structured and staffed as an AI-native team built to scale.
• By 12 months: Implementation, product, and leadership teams utilize self-service analytics for 90% of their data needs. The analytics engineering layer on BigQuery is healthy and fully owned, and the team actively utilizes AI-native practices. You are also overseeing data architecture in alignment with the predictive analytics strategy.

Responsibilities

Defining Vision and Structure for Performance Metrics Function

• Set the strategy, operating model, and priorities for the performance metrics function.
• Establish direction, standards, and expectations across the entire department.
• Build the function’s capability: Establish team structures, develop streamlined processes, and build cross-functional forums to ensure best-in-class data systems—especially as our strategy shifts toward predictive and self-service analytics. Develop your leaders through targeted coaching, clear leveling frameworks, and cultivating a team culture of rigor and continuous learning.
• Build systems and standards that ensure metrics actively support implementation teams in managing day-to-day program execution.
• Maintain a clear boundary and clean handoff with the evaluation team, ensuring the two functions complement rather than duplicate each other’s efforts.
• Partner with the central Tech team to ensure upstream pipelines meet necessary standards, and effectively direct the shared data engineer toward achieving those goals.

Data Engineering and Infrastructure – Build and own the technical foundation

• Design and own Educate!’s end-to-end data architecture, encompassing ingestion, warehousing, transformation, and delivery.
• Stand up a modern BI stack featuring self-serve reporting and automated pipelines that local country teams can readily utilize.
• Define the quality bar for the analytics engineering layer (dbt and the metrics layer on BigQuery) by ensuring accurate, complete, timely, consistent, valid, and traceable data, while holding your team accountable for maintaining that standard.
• Bring technical judgment to direct the work, assess quality (including the ability to challenge a data model or a pipeline), and execute effectively through your team.

Data Analytics Oversight – Turn data into decisions

• Collaborate with product, implementation, and performance metrics teams to define what gets measured and how-spanning operational metrics that support program implementation and classic monitoring of activities, outputs, and near-term outcomes.
• Establish a function deeply grounded in self-service analytics.
• Build the capability for the team to deploy multiple dashboards tailored to different audiences across the organization. Define and reinforce a collaborative process that scopes decisions at various levels and maps them to corresponding metrics.
• Ensure the performance metrics team transforms data into dashboards, insights, and decision tools that implementation, product, and leadership teams genuinely utilize.
• Raise data literacy across the organization to ensure teams actively champion and act on data rather than simply collecting it.
• Transition analysts from manual data analysis and reporting to focusing on the semantic layer of our data systems, ensuring it is optimized for AI. Similarly, usher in a shift from the performance metrics team providing manual analysis to a model of self-service analytics complemented by robust dashboards.
• Develop a strategy to improve frontline data usage in partnership with implementation teams, leveraging AI alongside both qualitative and quantitative data sources. The ultimate goal of this strategy is to ensure program implementation scales with consistency and quality.

Partner and Accelerate Analytics

• Act as the senior cross-functional partner to Tech, Evaluation, and Product, and a trusted advisor to the COO.
• Sponsor the function’s contribution to the cross