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Key Responsibilities and Required Skills for Data Collector

💰 $28,000 - $55,000

Data CollectionField ResearchData EntryGISSurvey Administration

🎯 Role Definition

The Data Collector is responsible for planning and executing data acquisition activities across digital and physical channels to support analytics, product development, operations, and research. This role combines fieldwork and remote data gathering with hands-on data validation, documentation, and delivery of clean datasets to downstream teams. The ideal candidate is organized, technically competent with common data collection tools (mobile apps, survey platforms, GPS/GIS devices, OCR, and basic scripting), and rigorous about data quality, metadata, and security.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Field Technician or Research Assistant
  • Customer Service / Data Entry Specialist
  • Internships in Research, Public Health, or Market Research

Advancement To:

  • Data Analyst
  • Research Associate / Field Supervisor
  • Data Engineer (entry-level ETL focus)
  • Quality Assurance / Data Quality Lead

Lateral Moves:

  • GIS Technician
  • Survey Designer
  • Operations Coordinator

Core Responsibilities

Primary Functions

  • Plan, schedule, and execute field and remote data collection campaigns, including sample selection, logistics coordination, and safety planning to ensure representative, reliable data acquisition for research and operational needs.
  • Design, test, and deploy survey instruments, questionnaires, and data capture forms using platforms such as SurveyCTO, Qualtrics, KoboToolbox, or REDCap, iterating on wording and flow to reduce respondent burden and measurement error.
  • Collect high-quality structured and unstructured data through interviews, observations, manual entry, sensor readouts, barcode/RFID scanning, photographs, audio/video recordings, and web or API-based extraction as required by the project.
  • Operate mobile data collection apps and devices (Android/iOS tablets, GPS units, barcode scanners), ensuring device configuration, offline caching, sync reliability, and secure transfer of collected data to central servers.
  • Perform real-time and batch data validation and cleaning activities including range checks, logic checks, deduplication, and missing-value assessment to ensure datasets meet quality standards before handoff.
  • Execute standardized data entry and transcription tasks with high accuracy from paper forms, audio interviews, or scanned documents into databases or data warehouses while maintaining chain-of-custody and version control.
  • Use SQL, Excel/Google Sheets, and scripting (Python or R) to extract, transform, and load (ETL) datasets; create reproducible data-cleaning pipelines and document transformation steps for auditability.
  • Annotate and label unstructured data (images, text, audio) for machine learning and analytics projects according to established coding guides and quality control protocols.
  • Implement and maintain metadata, data dictionaries, and provenance records for each collection wave to support discoverability and reproducibility of datasets.
  • Monitor data collection KPIs (response rates, completion times, error rates) and produce regular dashboards or status reports to stakeholders, recommending corrective actions when targets are not met.
  • Train, supervise, and quality-check the work of field enumerators, contractors, or crowdworkers, conducting spot checks and re-interviews to measure inter-rater reliability and reduce bias.
  • Conduct pre-deployment pilot tests and cognitive interviewing to validate question comprehension and data capture workflows, and revise instruments based on pilot findings.
  • Coordinate with IT and data engineering teams on server endpoints, API integrations, authentication, secure file transfer (SFTP), and automated ingest scripts to streamline collection-to-storage pipelines.
  • Scrape, aggregate, and normalize public web data or internal system logs using safe and compliant scraping practices, rate limiting, and API contracts while documenting source reliability.
  • Capture and integrate GIS/GPS coordinates and spatial metadata, create shape files or geojson exports, and collaborate with GIS analysts to ensure spatial accuracy and consistent coordinate reference systems.
  • Apply data privacy, confidentiality, and security practices (anonymization, encryption, access controls) to protect personally identifiable information (PII) and comply with organizational and legal requirements such as GDPR/HIPAA where applicable.
  • Prepare and deliver clean, well-documented datasets and codebooks to analysts, data scientists, and product teams, including clear notes on limitations, known biases, and recommended usage.
  • Troubleshoot technical issues during collection campaigns (device malfunctions, sync conflicts, power/coverage constraints) and implement contingency plans to minimize data loss.
  • Manage inventory and maintenance of data collection equipment and supplies, track assets, and coordinate repairs or replacements to ensure continuous operations.
  • Liaise with internal stakeholders (product managers, researchers, clients) to gather requirements, prioritize data needs, and clarify acceptance criteria for collected datasets.
  • Maintain quality assurance processes including double data entry, adjudication workflows, and inter-rater reliability assessments to achieve and document target accuracy levels.
  • Prepare and present findings from preliminary data reviews to project teams, highlighting data quality issues, sampling anomalies, and opportunities for improving collection protocols.
  • Ensure ethical data collection practices by obtaining informed consent, respecting community protocols, and being culturally sensitive during interactions with respondents.
  • Contribute to the development of standard operating procedures (SOPs), templates, and training materials for repeatable, scalable data collection operations.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Assist in vendor selection and management for outsourced data collection or annotation services.
  • Help prepare budgets, risk assessments, and timeline estimates for complex collection projects.

Required Skills & Competencies

Hard Skills (Technical)

  • Mobile data collection platforms: KoboToolbox, ODK, SurveyCTO, Qualtrics, REDCap.
  • Data cleaning and wrangling: Excel (advanced), Google Sheets, OpenRefine.
  • Querying and extraction: SQL (selects, joins, aggregations).
  • Scripting for automation and cleaning: Python (pandas) or R (dplyr, tidyr).
  • ETL and ingestion basics: understanding APIs, SFTP, JSON/CSV handling.
  • Web scraping and API use: BeautifulSoup, Selenium, requests, or equivalent tooling.
  • GIS/GPS handling: GPS devices, QGIS/ArcGIS basics, geojson/shapefile exports.
  • Data annotation and labeling platforms: Labelbox, CVAT, or proprietary annotation tools.
  • OCR and digitization: scanning best practices and OCR tools (Tesseract or cloud OCR).
  • Data quality and validation techniques: duplicate detection, range/logical checks, sampling audits.
  • Basic device and network troubleshooting for field hardware (tablet configuration, sync issues).
  • Familiarity with data privacy and security practices: anonymization, secure transfer, access controls.
  • Experience with CRM or database systems for data entry and maintenance (Salesforce, MS Access).
  • Version control basics for data and scripts: Git or documented file versioning practices.

Soft Skills

  • Exceptional attention to detail and strong commitment to data accuracy.
  • Clear verbal and written communication for instructions, reports, and consent scripts.
  • Strong organizational skills and ability to manage time and logistics across multiple collection sites.
  • Problem-solving mindset and ability to adapt to changing field conditions.
  • Cultural sensitivity and empathy when interacting with diverse respondents and communities.
  • Teamwork and ability to train, mentor, and supervise temporary field staff.
  • Ethical judgment and integrity when handling sensitive or personal data.
  • Resilience and composure working in field settings with limited resources.
  • Proactive reporting and escalation of issues with constructive recommendations.
  • Learning orientation and openness to adopt new tools and workflows quickly.

Education & Experience

Educational Background

Minimum Education:

  • High school diploma or equivalent; demonstrable experience in data entry, field work, or research can substitute.

Preferred Education:

  • Bachelor's degree in Data Science, Statistics, Geography, Public Health, Sociology, Computer Science, Environmental Science, or a related field.

Relevant Fields of Study:

  • Data Science / Analytics
  • Statistics / Mathematics
  • Geography / GIS
  • Public Health / Epidemiology
  • Sociology / Anthropology
  • Computer Science / Information Systems
  • Environmental Science / Ecology

Experience Requirements

Typical Experience Range: 0–3 years of relevant experience for entry-level roles; 2–5 years for mid-level positions involving supervision or complex technical workflows.

Preferred:

  • 1–3 years of hands-on experience with digital surveys, mobile data collection, or field enumeration.
  • Demonstrable experience with data cleaning, basic SQL or scripting, and metadata documentation.
  • Experience working in multi-stakeholder projects, research studies, or product teams preferred.
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