Data Transformation Specialist

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.Job opportunities

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