Director of Data Analytics
New
E
Educate!Youth employment
Flexibility — hybrid or fully remote work, depending on location. We will also consider applications from exceptional candidates based outside East Africa and the UK, where we do not have a physical office. For these candidates, the role will be remote and will involve substantial travel.Full-TimeDirector
Salary not disclosed
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Job Details
- Experience
- Around 7+ years in analytics, data, or M&E roles, including several years building and leading teams.
- Required Skills
- SQLData modelingBigQuerydbt
Requirements
- Bring around 7+ years in analytics, data, or M&E roles.
- Have several years of experience building and leading teams.
- Have managed managers, not only individual contributors.
- Have built or substantially grown a data or analytics team.
- Be able to speak to hiring, leveling, and developing analysts and engineers.
- Reason fluently about data models, transformations, and a metrics layer.
- Be comfortable with SQL, dbt-style workflows, and a data warehouse such as BigQuery.
- Be able to direct a data engineer and assess data pipeline quality.
- Understand data quality across accuracy, completeness, timeliness, consistency, validity, and traceability.
- Understand the distinction between monitoring and evaluation.
- Partner across functions and explain technical trade-offs to non-technical audiences, including executives.
Responsibilities
- Set the strategy, operating model, and priorities for the performance metrics function.
- Establish standards and expectations across the department, and build team structures, processes, and cross-functional forums.
- Coach and develop leaders through targeted coaching and clear leveling frameworks.
- Design and own end-to-end data architecture, including ingestion, warehousing, transformation, and delivery.
- Stand up a modern BI stack with self-serve reporting and automated pipelines for local country teams.
- Define and maintain quality standards for the dbt analytics engineering layer and BigQuery metrics layer.
- Collaborate with product, implementation, and performance metrics teams to define operational and monitoring metrics.
- Build dashboards, insights, and decision tools that implementation, product, and leadership teams use.
- Develop a strategy to improve frontline data usage, leveraging AI and qualitative and quantitative data sources.
- Partner with Tech, Evaluation, Product, and the COO on predictive analytics and cross-functional data science work.
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