Manager - Data and Insights

Purpose of the role
The Manager - Data and Insights will be embedded within the programme engagement and will own the day-to-day connection between programme questions, detailed data, digital systems, analysis and communication. The person will work closely with programme teams, CMS specialists and implementation partners to make data reliable, understandable and useful.
This is not a reporting-only or coordination-only MEL position. It requires someone who enjoys getting into the data personally, working through messy details, testing ideas, improving how information is captured and presented, and finding creative ways to explain what the evidence is saying. The role combines MEL thinking, hands-on analytics, data visualisation and storytelling, digital product ownership, quality assurance and team engagement.
What success looks like
· The person can move confidently from a row-level data issue to the wider programme narrative without losing accuracy or context.
· Datasets, indicators and dashboards are trusted because definitions, calculations and anomalies are examined carefully and resolved.
· Programme reviews use clear visuals and evidence-led narratives to explain what is changing, why it matters and what should happen next.
· The MIS reflects programme logic, is intuitive for users and is improved through close observation of how teams actually work.
· Programme and partner teams actively engage with the evidence because the role makes data accessible, relevant and useful to them.
Key responsibilitiesHands-on data management and analysis
· Work directly with programme datasets at a detailed level, including cleaning, joining, restructuring, validating and analysing data from multiple partners and systems.
· Build and maintain practical analysis files, reusable templates, data dictionaries and documented calculation logic so that outputs are reproducible and auditable.
· Interrogate unusual values, missing information, inconsistent totals and off-trend results rather than accepting aggregated reports at face value.
· Undertake exploratory and focused analyses to identify patterns, bottlenecks, outliers, emerging questions and areas requiring deeper investigation.
· Move comfortably between raw data, summary metrics and programme context while preserving a clear line back to the source.
MEL architecture and data logic
· Maintain and refine the programme results framework, indicator matrix and reporting architecture in alignment with the programme's Theory of Change and management priorities.
· Develop clear indicator reference sheets covering definitions, formulas, disaggregations, sources, verification, frequency, ownership and quality checks.
· Map programme indicators, targets and reporting responsibilities to relevant partners, geographies and implementation models.
· Support target-setting and revision by testing assumptions against historical performance, operating context, partner capacity and implementation conditions.
Technology and MIS product ownership
· Translate programme questions and user needs into clear workflows, business rules, data structures, validation logic, user stories and acceptance criteria for technology and data teams.
· Map partner formats into a common data structure and support master-data preparation, migration and standardisation across organisations and geographies.
· Lead programme-side testing of forms, calculations, QC workflows, dashboards and exports; document defects precisely and verify fixes before release.
· Act as a confident super-user of the MIS and related digital tools, supporting configuration, user onboarding, reporting cycles and routine problem-solving.
· Maintain a prioritised issue and change log and work constructively with developers, designers and data specialists through iterative improvements.
Data quality and attention to detail
· Own the reporting calendar and monitor completeness, timeliness and consistency across partners and geographies.
· Develop practical quality protocols covering automated checks, anomaly review, source verification, physical validation and correction approval.
· Trace errors to their source, coordinate resolution with programme and partner teams, and maintain an auditable correction trail.
· Check every output for calculation errors, denominator mismatches, misleading labels, inconsistent filters and gaps between the data and the narrative.
· Produce concise data-quality reports that distinguish isolated errors from recurring process or system problems.
Data visualisation, storytelling and decision support
· Analyse programme performance by partner, geography, cohort and implementation model, including trends, target variance and operational bottlenecks.
· Design clear, intuitive charts, dashboards and review packs that help different audiences see patterns and act on them quickly.
· Combine quantitative evidence, qualitative insights and field context into concise narratives that explain what is happening, why it matters and what questions remain.
· Experiment with new visual and narrative formats for leadership reviews, programme teams, partners, funders and other stakeholders.
· Respond to time-sensitive analytical questions with accurate, decision-ready outputs rather than generic data dumps.
Team engagement, learning and creative problem-solving
· Build strong working relationships with programme and partner teams so that questions, uncertainties and data issues surface early.
· Facilitate evidence-based reviews that move naturally from insight to decisions, named actions, timelines and follow-up.
· Identify positive deviance, persistent underperformance and emerging risks, and help teams test explanations and response options.
· Use curiosity and creativity to design rapid analyses, assessments or learning deep-dives when routine information is insufficient.
· Make technical concepts accessible to non-technical colleagues and create space for teams to challenge, interpret and use the evidence.
Field grounding, capability building and institutionalisation
· Undertake periodic field visits with programme, regional and field teams to understand how data is generated, test whether tools reflect field practice and triangulate reported results.
· Design practical onboarding, guidance and hands-on support on data entry, quality control, reporting, dashboards and interpretation.
· Observe how teams use tools and outputs, then simplify workflows, improve interfaces and adapt support based on real user behaviour.
· Strengthen programme and partner capability so that data quality, analysis and use improve at source rather than only during central consolidation.
· Document definitions, decisions, workflows, analytical logic and learning so that the system does not depend on individual memory.
Data protection, ethics and safeguarding
· Apply data-minimisation, purpose-limitation and role-based access principles across collection, analysis, reporting and evidence verification.
· Ensure that personal and sensitive information is not collected beyond what is necessary and explicitly approved through a lawful, safeguarded protocol.
· Support appropriate consent, retention, deletion and access-control practice

Work arrangement
No