Data Transformation Specialist – Family Legacy

Job Description

About the job Data Transformation Specialist

POSITION OVERVIEW

The Program Data Transformation Specialist leads initiatives to evolve Family Legacy’s data ecosystem from fragmented collections to integrated systems that enhance decision-making and program effectiveness. This position bridges analytical needs with technological capabilities to build cohesive data architectures and user-centered applications that directly strengthen our ability to serve vulnerable children. The role requires a systems thinker who can transform scattered information into unified insights while building the organization’s capacity to collect, manage, and utilize data effectively.

REPORTING RELATIONSHIPS

Reports to:

• Primary: Monitoring, Evaluation and Research Manager
• Functional: ICT Manager

Collaborates with: Program directors, field staff, data collectors, and technology implementers

PURPOSE

Family Legacy exists to glorify God by empowering vulnerable Zambian children to live to the fullest expression of their God-given worth and potential. Through redemptive child development, we serve and educate vulnerable children in a holistic manner: spiritually, intellectually, physically, and emotionally.

The Program Data Transformation Specialist designs and implements solutions that transform our program data ecosystem from distributed collections into integrated systems that enhance program effectiveness and child outcomes. This position systematically addresses data fragmentation by developing unified databases, creating user-friendly collection tools, and establishing workflows that connect information across programs. By building coherent data architectures and application interfaces, this role ensures that critical program information is accessible, reliable, and actionable directly enhancing our capacity to measure impact and improve services to vulnerable children.

DIMENSIONS OF THE ROLE

The Program Data Transformation Specialist works at the intersection of program monitoring, data analysis, and technology implementation converting program measurement needs into practical digital solutions while ensuring data quality and accessibility. This position requires a deep understanding of how field-level data collection connects to program evaluation and decision-making, with the ability to design systems that function effectively in environments with varying connectivity and user technical proficiency. The role demands both analytical rigor and human-centered design thinking to create solutions that collect meaningful data while remaining practical for frontline implementation.

KEY RESPONSIBILITIES

1. Data System Architecture and Integration (30%)

• Design and implement a unified data architecture that consolidates information from currently fragmented sources
• Develop migration strategies to transition data from Google Sheets, Dropbox, and other distributed locations into structured databases
• Create data mapping frameworks that establish relationships between previously isolated datasets
• Implement data validation protocols that ensure consistency across integrated systems
• Develop automated synchronization processes between field collection tools and central databases
• Design scalable database structures that accommodate program growth
• Create standardized data models that enable cross-program analysis
• Establish metadata frameworks that enhance searchability and context
• Implement data versioning systems that maintain historical records
• Develop integration pathways between program-specific and organization-wide systems

2. Custom Application Development and Implementation (25%)

• Design and develop field-appropriate data collection applications that improve data quality and timeliness
• Create user-friendly dashboards that provide real-time program performance visualization
• Implement mobile solutions that function effectively in environments with limited connectivity
• Develop offline-capable applications that synchronize when connectivity is available
• Design workflow applications that standardize program processes
• Implement beneficiary tracking systems that monitor child progress across multiple programs
• Create monitoring tools that streamline field data collection
• Develop solutions that minimize duplicate data entry
• Implement feedback collection mechanisms that capture beneficiary perspectives
• Design case management applications that enhance coordination of child services

3. Data Quality and Governance (15%)

• Establish organization-wide data standards and definitions to ensure consistency
• Implement automated quality control processes that identify anomalies and inconsistencies
• Develop comprehensive data dictionaries that standardize terminology across programs
• Create data cleaning protocols and tools for legacy and ongoing data
• Implement classification systems that improve data organization
• Develop permission structures that balance accessibility with privacy protection
• Create longitudinal data linkage protocols that maintain child records over time
• Develop procedures for managing sensitive child information
• Implement comprehensive data documentation systems
• Create data quality scorecards to track improvements over time

4. Analytics and Reporting Solutions (15%)

• Design and implement automated reporting systems that reduce manual compilation
• Develop interactive visualization tools that make data accessible to non-technical users
• Create standardized report templates that ensure consistency in program reporting
• Implement advanced analytics capabilities that identify patterns in program data
• Develop predictive models that support early intervention in child development
• Create outcome tracking systems that measure progress against goals
• Implement comparative analysis tools that identify program improvement opportunities
• Develop trend analysis capabilities that monitor changes over time
• Create beneficiary segmentation frameworks that enable targeted interventions
• Implement impact measurement dashboards aligned with organizational objectives

5. User Adoption and Capacity Building (10%)

• Develop and deliver training programs that build staff capacity with new data systems
• Create user documentation and support resources tailored to different technical proficiency levels
• Implement user testing protocols that ensure solutions meet field requirements
• Develop phased roll-out strategies that support successful adoption
• Create super-user programs that establish in-house expertise
• Develop context-appropriate training materials for field staff
• Implement user feedback mechanisms that inform continuous improvement
• Create troubleshooting resources that support field-level problem resolution
• Develop data literacy programs that enhance staff analytical capabilities
• Implement change management strategies that support transition to new systems

6. Research and Continuous Improvement (5%)

• Research emerging data collection technologies relevant to development contexts
• Evaluate potential solutions against organizational constraints and requirements
• Conduct user research to identify pain points in current data processes
• Develop measurement frameworks for system effectiveness
• Implement systematic user feedback collection to guide improvements
• Create innovation testing protocols for evaluating new approaches
• Develop efficiency metrics that quantify process improvements
• Research best practices in development-sector data management
• Conduct regular system reviews to identify enhancement opportunities
• Implement A/B testing methodologies for interface improvements

QUALIFICATIONS AND EXPERIENCE REQUIRED

Educational Requirements

• Bachelor’s degree in Information Systems, Data Science, Computer Science, or related field
• Project Management Certification will be an advantage
• Training in database architecture and management
• Certifications in relevant data or application development technologies

Experience

• Minimum 4 years experience working with program data in development organizations
• Demonstrated success consolidating distributed data into unified systems
• Experience developing practical applications for challenging implementation environments
• Background in monitoring and evaluation data systems
• Experience implementing mobile data collection solutions
• Proven track record transforming manual processes into digital workflows
• Experience working with vulnerable populations data preferred
• Background in designing user-centered solutions for varying technical proficiency levels

Technical Knowledge

• Strong database design and management capabilities
• Expertise in data migration and integration methodologies
• Proficiency in application development for resource-constrained environments
• Understanding of data governance principles and implementation
• Knowledge of data quality assurance methodologies
• Familiarity with offline-first application architecture
• Understanding of data security and privacy requirements
• Proficiency with data visualization techniques and tools
• Knowledge of development sector data standards
• Understanding of appropriate technology principles

Professional Skills

• Excellence in translating program needs into technical requirements
• Strong analytical thinking and problem-solving abilities
• Outstanding data modeling and systems thinking capabilities
• Excellent communication skills, particularly explaining technical concepts
• Strong documentation and knowledge management abilities
• Ability to balance ideal solutions with practical constraints
• Excellent stakeholder management and requirement gathering skills
• Strong project management capabilities
• Ability to work effectively with both technical and non-technical teams
• Cultural sensitivity and contextual awareness

CORE COMPETENCIES

Systems Architecture Thinking

• Ability to design cohesive data ecosystems from fragmented components
• Excellence in identifying integration pathways between disparate systems
• Skill in developing scalable data models that accommodate future needs
• Capacity to balance immediate solutions with long-term architecture goals

Human-Centered Design

• Strong ability to develop solutions based on user needs and constraints
• Excellence in creating interfaces appropriate for varying technical literacy levels
• Skill in optimizing user experiences for challenging implementation environments
• Capacity to design solutions that minimize burden on data collectors

Data Quality Leadership

• Ability to establish and maintain high data quality standards
• Excellence in designing validation processes that ensure reliable information
• Skill in developing practical quality assurance protocols
• Capacity to build organizational culture that values data integrity

Adaptive Problem Solving

• Strong ability to develop creative solutions for resource-constrained settings
• Excellence in modifying approaches based on field realities
• Skill in balancing technical best practices with practical implementation
• Capacity to identify appropriate technology solutions for challenging contexts

Implementation Excellence

• Ability to manage successful transitions from concept to operational systems
• Excellence in phased implementation approaches that build on successes
• Skill in supporting users through system transitions
• Capacity to maintain focus on improving child outcomes through better data

WORKING ENVIRONMENT AND CONDITIONS

The position is based at the FLMZ Ibex Hill Office and requires:

• Regular field visits to understand data collection realities at Legacy Academies and programs
• Ability to develop solutions that function in environments with connectivity challenges
• Flexibility to adapt approaches based on user feedback and field constraints
• Commitment to creating systems that ultimately improve services to vulnerable children
• Willingness to balance technical ideals with practical implementation realities

PERFORMANCE MEASURES

Success in this role will be measured by:

• Successful consolidation of fragmented data into unified, accessible systems
• Improvement in data quality, completeness, and timeliness
• Increased efficiency in program monitoring and reporting processes
• Development and adoption of practical field data collection applications
• Reduction in manual data processing requirements
• Enhanced analytical capabilities across the organization
• Improved data accessibility for decision-making
• User satisfaction with implemented solutions
• Contribution to improved program outcomes through better data utilization
• Development of sustainable, maintainable data systems