Data & Systems Analyst

DATA & SYSTEMS ANALYST 

ABOUT MORSBY, GORMAN, MCCARTHY LLC

Morsby, Gorman, McCarthy LLC is developing Project Guardian as a multidisciplinary, project-based initiative focused on infrastructure planning, implementation readiness, policy research, partnership development, evidence management, risk analysis, communications, and program coordination.

Project Guardian’s initial work centers on a proposed Kenya-focused infrastructure pilot. Participants will contribute to research, planning, analysis, documentation, and professional-quality project materials intended to help clarify the pilot’s feasibility, evidentiary foundation, funding requirements, implementation conditions, and potential pathways toward responsible replication or expansion.

Participation does not represent employment by, appointment to, or endorsement from any government, university, funder, development institution, community, or prospective partner. Project Guardian is an independent initiative of Morsby, Gorman, McCarthy LLC unless a separate written agreement states otherwise.

PROGRAM TERMS

• Program dates: October 19, 2026–January 29, 2027
• Holiday pause: December 19, 2026–January 3, 2027, during which no routine work is expected
• Fully remote and asynchronous-first
• Normally 5–10 hours per week
• Work exceeding 10 hours in a week requires advance written approval
• Participants should not ordinarily exceed approximately 15 hours in any week
• Unpaid educational internship
• Academic credit may be available only when approved by the participant’s institution
• No promise or expectation of future employment
• Reasonable academic and accessibility accommodations will be considered
• Participation is subject to applicable confidentiality, data-handling, authorship, attribution, acceptable-use, and release requirements

EDUCATIONAL STRUCTURE

This is a structured, project-based learning experience. Participants receive defined learning objectives, supervised assignments, written or documented feedback, revision opportunities, and exposure to data management, systems analysis, information governance, infrastructure research, public policy, and multidisciplinary program work.

Assignments are designed to develop transferable professional competencies through applied work of educational value. Participants may produce portfolio-appropriate materials, excerpts, or descriptions subject to confidentiality, intellectual-property, attribution, data-protection, and release requirements.

Inclusion of any project dataset, schema, data dictionary, system map, requirements document, screenshot, process, analysis, or deliverable in a portfolio requires prior written authorization. Authorized portfolio materials may require redaction, aggregation, anonymization, modification, or explanatory limitations.

Academic credit may be available only when approved by the participant’s institution. Participants are responsible for initiating and satisfying their institution’s credit-approval process. Morsby, Gorman, McCarthy LLC will reasonably cooperate with legitimate institutional documentation, learning-agreement, supervision, evaluation, and verification requirements when provided with sufficient notice.

Academic credit is not guaranteed and is not required for participation. The availability, amount, classification, and conditions of credit are determined solely by the participant’s institution.

The internship is intended to complement academic study and professional development. Participants will not displace regular employees or independently perform production system administration, cybersecurity operations, legal compliance determinations, or functions requiring licensed engineering, financial, privacy, security, or other specialized professional judgment.

ROLE PURPOSE

The Data & Systems Analyst helps Project Guardian define how information should be structured, documented, validated, connected, governed, and used across its research and planning activities.

The analyst may support data inventories, requirements analysis, data dictionaries, process and system mapping, schema development, dataset documentation, quality assessment, controlled integration planning, and information-governance practices.

The role focuses on understanding how information moves through a multidisciplinary project and how systems can support accurate, traceable, proportionate, and responsible use of that information.

This role is distinct from the Technical Data & Automation Analyst. The Data & Systems Analyst primarily focuses on requirements, data structure, documentation, quality, governance, and workflow design. Coding or automation may be used for bounded educational assignments but is not the defining responsibility of this position.

This is a hands-on contributor internship, not a production database-administrator, systems-administrator, cybersecurity, or independent decision-making position. Formal supervisory experience is not required.

Analysts will receive defined assignments, access to appropriate data and project context, documented feedback, revision opportunities, and progressively more complex work where supported by demonstrated readiness and educational value.

LEARNING OBJECTIVES

By the conclusion of the internship, the participant should be able to:

• Translate a project or research need into clearly documented data and system requirements
• Distinguish data sources, datasets, fields, records, entities, relationships, identifiers, metrics, and derived values
• Develop data inventories, data dictionaries, field definitions, schemas, and system maps
• Document provenance, ownership, sensitivity, permitted use, refresh status, limitations, and quality concerns
• Evaluate structured information for completeness, consistency, validity, uniqueness, timeliness, and usability
• Identify duplicates, missing values, incompatible definitions, broken relationships, and undocumented transformations
• Map how information moves among sources, working files, analyses, trackers, and deliverables
• Explain the distinction among a source system, working dataset, analytical dataset, reporting layer, and published output
• Develop proportionate recommendations for improving data structure, workflow, documentation, and controls
• Apply data-minimization, least-privilege, purpose-limitation, and responsible-access principles
• Recognize when privacy, cybersecurity, legal, contractual, intellectual-property, or records questions require specialized review
• Communicate data and systems findings clearly to technical and nontechnical collaborators
• Incorporate feedback into revised documentation, models, and recommendations
• Work across disciplines without overstating technical certainty or authority

PRIMARY RESPONSIBILITIES

Requirements Analysis

• Clarify the purpose, intended users, decisions, outputs, sources, and limitations associated with assigned data or system needs
• Translate approved project questions into structured data requirements
• Identify required fields, definitions, relationships, update expectations, validation needs, and access considerations
• Distinguish required information from optional or speculative collection
• Identify conflicting stakeholder terminology, duplicated requirements, and unresolved definitions
• Document functional needs, information requirements, assumptions, exclusions, dependencies, and acceptance criteria
• Identify questions requiring input from research, operations, technical, policy, funding, evidence, risk, or communications teams
• Avoid presenting preliminary requirements as approved specifications
• Maintain traceability between approved objectives and proposed data or system requirements

Data Inventory and Source Mapping

• Identify authorized data sources, datasets, trackers, registers, documents, and structured information used in assigned work
• Record source ownership, provenance, format, access conditions, update frequency, geographic scope, timeframe, and known limitations
• Distinguish authoritative source records from working copies, derived datasets, summaries, and manually transcribed information
• Identify unavailable, inaccessible, duplicated, outdated, incomplete, or undocumented sources
• Record whether information is public, licensed, internally authorized, restricted, preliminary, or externally supplied
• Map relationships among source information, transformations, analyses, metrics, and deliverables
• Coordinate with the Knowledge and Data-Room Manager on storage, classification, version, and source-record requirements
• Avoid collecting or copying information merely because it may be useful later

Data Dictionaries and Definitions

• Develop and maintain data dictionaries for assigned datasets or trackers
• Document field names, plain-language definitions, data types, valid values, formats, units, source systems, calculation rules, ownership, and limitations
• Identify ambiguous, inconsistent, overlapping, or undocumented terms
• Reconcile terminology with approved project definitions
• Distinguish raw, entered, standardized, calculated, modeled, and manually reviewed values
• Document missing-value conventions, status labels, confidence indicators, and applicable validation rules
• Identify definitions that could create misleading comparisons across places, time periods, projects, or populations
• Submit material definitions and calculation rules for appropriate review
• Maintain revision history when definitions change

Conceptual Data Modeling

• Help develop supervised conceptual or logical models describing relevant entities, attributes, identifiers, and relationships
• Create appropriately scoped schema diagrams or relationship maps
• Identify one-to-one, one-to-many, many-to-many, hierarchical, temporal, and geographic relationships where relevant
• Document assumptions concerning identifiers, uniqueness, granularity, and record boundaries
• Identify where inconsistent grain or duplicated entities could cause analytical errors
• Avoid representing a conceptual model as a production-ready database design without technical review
• Coordinate with technical contributors when a model may inform automation or implementation
• Preserve sufficient documentation for authorized reviewers to understand the model’s purpose and limitations

Data Quality Assessment

• Conduct supervised reviews for completeness, validity, consistency, uniqueness, accuracy, timeliness, and referential integrity
• Develop proportionate quality checks connected to the intended use of the information
• Identify missing values, duplicates, invalid formats, inconsistent units, outliers, conflicting definitions, and broken relationships
• Distinguish an unusual value from a confirmed error
• Record identified issues, affected fields or records, materiality, possible causes, and recommended next steps
• Avoid silently correcting source information without an approved method and appropriate traceability
• Maintain correction, transformation, and exception records
• Coordinate with the Evidence and Quality Assurance Lead on material data-quality concerns
• Clearly communicate when available data are insufficient for an intended conclusion or decision

Data Cleaning and Standardization Planning

• Recommend supervised approaches for standardizing formats, categories, units, dates, identifiers, locations, and missing values
• Preserve original values or source records where required
• Document transformation rules and the reasons for them
• Distinguish deterministic cleaning from judgment-based classification
• Flag transformations that could alter substantive meaning
• Avoid imputing, estimating, merging, deleting, or recoding material information without an approved method
• Test proposed rules on appropriately limited data where authorized
• Record unresolved exceptions and ambiguous cases
• Prepare clear handoff documentation for any authorized technical implementation

Systems and Workflow Mapping

• Map how information enters, moves through, and exits an approved project workflow
• Identify users, systems, files, handoffs, decisions, approvals, outputs, and dependencies
• Distinguish current workflows from proposed future processes
• Identify duplicated entry, manual rework, unclear ownership, uncontrolled copies, inconsistent definitions, and missing review points
• Recommend proportionate workflow improvements based on documented needs
• Consider usability, accessibility, confidentiality, maintenance burden, and continuity
• Coordinate with the Project & Operations Analyst on operational process implications
• Avoid recommending a new platform or system without considering governance, cost, access, support, migration, and long-term ownership

Integration and Interoperability Planning

• Identify approved situations in which information may need to move among documents, spreadsheets, forms, databases, analytical tools, or reporting outputs
• Document source and destination fields, formats, identifiers, timing, dependencies, and validation requirements
• Identify incompatible schemas, conflicting definitions, missing identifiers, and potential information loss
• Develop supervised mapping tables and integration requirements
• Recommend validation and reconciliation steps
• Distinguish conceptual integration planning from authorized production implementation
• Avoid transferring information to new systems without approval
• Coordinate with the Technical Data & Automation Analyst where bounded technical implementation is appropriate

Information Governance and Access Support

• Help document data ownership, stewardship, classification, sensitivity, permitted use, access, retention, and disclosure requirements
• Apply approved least-privilege, purpose-limitation, and data-minimization principles
• Identify datasets containing unnecessary personal, confidential, proprietary, or restricted information
• Recommend aggregation, anonymization, redaction, synthetic examples, or reduced collection where appropriate
• Distinguish possession of information from authority to use, modify, disclose, retain, or delete it
• Escalate privacy, intellectual-property, licensing, contractual, confidentiality, cybersecurity, and records-retention questions
• Avoid making independent legal or compliance determinations
• Coordinate with the Knowledge and Data-Room Manager on controlled storage and access records

Documentation and Reproducibility

• Maintain clear documentation of sources, field definitions, transformations, assumptions, validation checks, limitations, and unresolved issues
• Preserve appropriate versions and change history
• Develop data-readme files, system notes, process descriptions, and handoff materials
• Ensure that an authorized reviewer can understand what a dataset represents and how it was prepared
• Distinguish reproducible steps from undocumented manual judgment
• Identify dependencies on individual knowledge, personal storage, or unavailable tools
• Cross-reference relevant research, evidence, operational, and technical records
• Avoid representing incomplete documentation as sufficient for production, audit, or external due diligence

Cross-Functional Collaboration

• Support data and systems needs across research, operations, policy, funding, risk, evidence, technical, and communications workstreams
• Clarify the intended question and use before recommending a data structure or system change
• Communicate definitions, limitations, quality concerns, and dependencies clearly
• Coordinate with researchers concerning source meaning and context
• Coordinate with technical contributors concerning implementation feasibility
• Coordinate with visualization contributors concerning definitions, aggregation, and reporting needs
• Escalate material conflicts in definitions, sources, requirements, or permitted use
• Maintain usable handoff records when responsibility changes

SUPERVISION, FEEDBACK, AND REVISION

The Data & Systems Analyst will receive assignments, guidance, and feedback from designated project or workstream leadership.

The analyst may coordinate closely with the Evidence and Quality Assurance Lead, Knowledge and Data-Room Manager, Monitoring, Risk and Continuity Lead, Deputy Program and Operations Manager, and relevant technical or research contributors.

The educational workflow is expected to include:

• Initial orientation to Project Guardian’s scope, information environment, standards, and limitations
• Defined assignments connected to the role’s learning objectives
• Access to appropriate templates, examples, source requirements, and project context
• Scheduled or milestone-based supervisory review
• Written, annotated, or otherwise documented feedback
• Opportunities to ask questions and clarify expectations
• Reasonable opportunities to revise substantive work
• Progressively more complex assignments where supported by demonstrated readiness
• Periodic reflection on professional development and learning progress
• A final review or evaluation where appropriate

Feedback may address requirements reasoning, data structure, definitions, documentation, quality assessment, workflow analysis, governance judgment, clarity, accuracy, collaboration, and the participant’s ability to incorporate revisions.

EXPECTED EDUCATIONAL OUTPUTS

Depending on project needs, participant readiness, workstream placement, source availability, and supervisory direction, outputs may include:

• Data and information requirements documents
• Data-source and dataset inventories
• Source-to-output information maps
• Data dictionaries and field-definition registers
• Conceptual or logical data models
• Entity-relationship or schema diagrams
• System and information-flow maps
• Data-quality assessment records
• Validation-rule and exception documentation
• Standardization and cleaning recommendations
• Source-to-destination mapping tables
• Integration and interoperability requirements
• Governance, ownership, access, and sensitivity records
• Data-readme and methodology documentation
• Workflow-improvement recommendations
• Data limitations and unresolved-question logs
• Handoff and reproducibility documentation
• Substantive original analysis and technical writing

Outputs are educational and developmental materials. Their production does not guarantee that they will be adopted, deployed, externally distributed, certified, or used in a production environment.

PERFORMANCE AND LEARNING ASSESSMENT

Progress may be assessed according to:

• Clarity and completeness of requirements
• Accuracy and usability of data dictionaries and system documentation
• Quality of conceptual modeling and workflow analysis
• Ability to distinguish source, working, derived, analytical, and reporting information
• Identification and documentation of data-quality risks
• Transparency concerning assumptions, transformations, limitations, and unresolved questions
• Appropriate application of data-minimization and access principles
• Respect for confidentiality, licensing, privacy, local context, and stakeholder authority
• Ability to communicate technical concepts to nontechnical collaborators
• Quality of source, version, validation, and reproducibility records
• Appropriate escalation of issues requiring specialist or leadership review
• Ability to receive, evaluate, and incorporate feedback
• Reliability in meeting agreed milestones or communicating constraints early
• Effectiveness in multidisciplinary collaboration
• Growth against the stated learning objectives

Assessment is intended to support learning and program administration. It is not a licensed professional examination or certification in systems analysis, database administration, data governance, privacy, cybersecurity, engineering, or any other professional field.

PREFERRED QUALIFICATIONS

Candidates may be undergraduate or graduate students, postdoctoral researchers, recent graduates, alumni, or early-career professionals. Relevant classroom, volunteer, internship, military, community, entrepreneurial, or professional experience may be considered. No single academic major is required.

Relevant academic areas may include information systems, data science, business analytics, computer science, information science, library science, statistics, public policy, public administration, project management, business, engineering, economics, or related interdisciplinary fields.

Strong candidates will demonstrate:

• Strong analytical, organizational, and written-communication skills
• Interest or experience in data management, systems analysis, information architecture, data governance, business analysis, or research operations
• Ability to translate ambiguous information needs into structured requirements
• Comfort working with spreadsheets, tables, records, definitions, and structured information
• Ability to identify inconsistencies, missing information, duplicates, and unclear ownership
• Attention to data provenance, documentation, quality, confidentiality, and permitted use
• Ability to explain technical or structural issues to nontechnical collaborators
• Willingness to document methods, accept feedback, and revise substantive work
• Respect for local context, disciplinary boundaries, and legitimate stakeholder authority
• Reliability when collaborating across disciplines and time zones

Familiarity with spreadsheets, relational-data concepts, databases, data dictionaries, workflow mapping, SQL, Python, R, low-code tools, or collaborative data platforms may be helpful but is not required unless relevant to a specific assignment.

Prior experience relating to Kenya, Africa, infrastructure, international development, or public-sector data may be helpful but is not required.

Applicants are not expected to possess professional database-administration, cybersecurity, privacy, engineering, or systems-architecture credentials.

APPLICATION MATERIALS

Applicants should submit through Handshake:

• A current résumé
• A cover letter explaining their interest in Project Guardian and describing relevant data, systems, information-management, analytical, academic, or transferable experience
• One relevant work sample, such as a data dictionary, schema, system map, requirements document, data-quality analysis, workflow analysis, spreadsheet model, database project, research-data plan, or comparable academic, professional, volunteer, or independent work

Applicants should remove or redact confidential, proprietary, personally identifiable, restricted, privileged, licensed, or third-party information from submitted samples.

If the applicant’s strongest work was completed collaboratively, the applicant should briefly identify their individual contribution.

A prior professional work sample is not required. Academic, volunteer, independent, simulated, synthetic-data, or appropriately redacted work is acceptable.

SELECTION PROCESS

Initial screening will be asynchronous. Applications will be assessed based on demonstrated alignment, structural reasoning, analytical readiness, documentation quality, judgment, learning potential, and the relevance of submitted materials.

Finalists may be invited to complete a brief, time-limited educational evaluation exercise using hypothetical or synthetic information. The exercise may ask the applicant to organize a small dataset, draft several field definitions, identify data-quality issues, map a simple information flow, or document requirements for a proposed tracker.

Any selection exercise will be designed solely to evaluate the applicant’s reasoning and communication. It will not involve actual confidential project information, be used as uncompensated production work, or be incorporated into Project Guardian deliverables without separate written permission and appropriate arrangements.

Live conversations will be limited to finalists or circumstances requiring clarification. References may be requested selectively. Reasonable accommodations are available during the selection process.

Priority application deadline: October 2, 2026. The posting may close earlier or later based on application volume, institutional review timing, or program needs.

DATA, SYSTEM, AND SECURITY LIMITATIONS

This internship does not provide unrestricted access to Project Guardian information or systems. Access will be limited to what is reasonably necessary for approved assignments.

Unless separately authorized, the analyst may not:

• Access production, financial, personnel, authentication, or administrative systems
• Request or use another person’s credentials
• Change permissions or security settings
• Deploy databases, integrations, automations, software, or infrastructure
• Scrape websites or access data in violation of terms, licenses, or technical controls
• Circumvent access restrictions
• Conduct vulnerability testing, penetration testing, or security monitoring
• Collect unnecessary personal or sensitive information
• Transfer project data to personal accounts, devices, repositories, or unapproved platforms
• Merge, delete, recode, anonymize, or disclose material information without an approved method
• Represent a conceptual model or prototype as production-ready

Any technical testing must use approved systems, synthetic or appropriately authorized data, bounded scope, and supervised procedures.

ELIGIBILITY AND INSTITUTIONAL CONDITIONS

Applicants must be able to participate remotely during the stated program period and communicate reliably in an asynchronous-first environment.

Students seeking academic credit must contact their institution and initiate any required approval, registration, learning-agreement, or internship-verification process. Institutional approval and academic credit are not guaranteed.

Where required, participation may be conditioned on completion of appropriate institutional documentation, confidentiality agreements, information-handling acknowledgments, intellectual-property terms, conflict disclosures, acceptable-use requirements, or other reasonable program documentation.

International students and other applicants are responsible for confirming with their institution or qualified adviser whether participation is consistent with applicable academic, immigration, authorization, funding, or institutional requirements. Morsby, Gorman, McCarthy LLC does not provide immigration, visa, tax, legal, or employment-authorization advice.

CONFIDENTIALITY, ATTRIBUTION, AND RESPONSIBLE TOOL USE

Participants may encounter confidential, preliminary, proprietary, sensitive, licensed, or access-controlled information. Such information may be used only for authorized program purposes and handled according to applicable instructions.

Participants must:

• Protect confidential, personal, proprietary, licensed, and restricted information
• Use only authorized systems, accounts, storage locations, repositories, and transfer methods
• Access only materials reasonably necessary for assigned work
• Avoid uploading confidential, personal, proprietary, licensed, or restricted materials to unapproved third-party platforms
• Preserve source, definition, transformation, validation, review, and version records
• Distinguish original work from quoted, adapted, collaborative, template-based, or tool-assisted material
• Disclose material use of generative artificial intelligence or other automated tools when requested
• Independently verify definitions, calculations, classifications, mappings, summaries, and system recommendations
• Avoid fabricating sources, fields, data, requirements, quality results, tests, approvals, or findings
• Obtain approval before externally sharing project materials or including them in a portfolio
• Report suspected confidentiality, access, integrity, security, or information-handling problems promptly

Automated tools may support learning, brainstorming, documentation, analysis, or bounded prototyping only when permitted. They may not replace the participant’s judgment, validation, confidentiality obligations, or responsibility for submitted work.

Restricted information may not be entered into public or unauthorized artificial-intelligence systems. Automated transformations, classifications, integrations, or data-quality conclusions must receive appropriate human review.

PROFESSIONAL AND REGULATORY LIMITATIONS

This internship does not authorize the participant to provide or represent that they are providing legal, privacy, cybersecurity, engineering, financial, regulatory, compliance, database-administration, or systems-architecture advice or certification.

Data models, requirements, quality reviews, system maps, and integration recommendations are educational and developmental materials. They are not production specifications, security audits, privacy assessments, regulatory determinations, engineering certifications, or guarantees of completeness, accuracy, security, interoperability, compliance, or performance.

Questions requiring specialized professional judgment must be identified and routed for qualified review.

REPORTING AND AUTHORITY

The analyst will receive assignments and feedback from designated project or workstream leadership. Overall operational coordination remains with the Deputy Program and Operations Manager.

The analyst may:

• Complete approved internal data, requirements, documentation, quality, and workflow assignments
• Develop inventories, dictionaries, conceptual models, mappings, and recommendations
• Request clarification and authorized project information
• Identify data-quality, governance, access, workflow, and integration concerns
• Recommend bounded improvements or additional review
• Collaborate with assigned internal participants
• Revise work in response to documented feedback

The analyst may not independently:

• Obtain unrestricted or administrative access
• Change permissions, security controls, or production configurations
• Deploy systems, databases, integrations, software, or automations
• Transfer, publish, disclose, or release project data
• Contact external stakeholders without approval
• Make legal, financial, employment, procurement, partnership, technical, or contractual commitments
• Represent the company, project, university, or any institution externally
• Approve final technical, privacy, security, legal, or governance conclusions
• Promise implementation, funding, partnership, academic credit, employment, publication, or any project outcome
• Authorize another participant to exercise any of these powers

Final decisions concerning access, architecture, security, privacy, data governance, deployment, external disclosure, and system adoption remain with authorized program leadership and, where necessary, qualified professionals.

All external outreach, data transfers, deployments, representations, commitments, and releases require prior authorization.

EQUAL CONSIDERATION AND ACCOMMODATIONS

Morsby, Gorman, McCarthy LLC intends to consider applicants based on role-related qualifications, demonstrated readiness, learning potential, and program needs. Reasonable accommodations may be requested for the application process or internship experience.

Applicants are encouraged to describe relevant academic, professional, volunteer, military, community, entrepreneurial, caregiving, independent, or lived experience when it demonstrates transferable skills applicable to the role.