Key Responsibilities and Required Skills for Web Research Technician

🎯 Role Definition

The Web Research Technician is responsible for locating, extracting, verifying and organizing high-value web-based information to support product teams, market intelligence, sales lead generation, compliance checks and research initiatives. This role blends advanced online search techniques, web scraping and API usage with rigorous data quality control to deliver trusted datasets, competitive insights and timely research deliverables for internal stakeholders. The Web Research Technician must be comfortable working with both manual investigative methods and automated data-collection tools, documenting sources and maintaining compliance with privacy and copyright regulations.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Web Researcher or Research Assistant
Data Entry Specialist with web research exposure
Market Research Intern or Sales Operations Assistant

Advancement To:

Senior Web Research Technician / Lead Web Researcher
Competitive Intelligence Analyst / Market Intelligence Analyst
Data Analyst or Data Operations Lead

Lateral Moves:

Sales Enablement / Lead Generation Specialist
Content Researcher / Product Research Analyst

Core Responsibilities

Primary Functions

Conduct advanced online research using Boolean logic, advanced search operators, deep site navigation, social media platforms and specialized databases to identify and extract business-critical information and digital assets for product, market and sales teams.
Design, execute and maintain web scraping workflows using tools such as Python (Scrapy, BeautifulSoup), Selenium, Puppeteer or commercial scraping platforms to collect structured datasets from websites, portals and public APIs while ensuring stability and efficiency.
Create, standardize and document repeatable data collection procedures and search playbooks (including search strings, source lists, scraping schedules and error-handling steps) so that research can be reproduced and audited by other team members.
Validate and verify online findings through cross-referencing multiple primary and secondary sources, confirming company details, contact information, pricing, product specs, and metadata to ensure 95%+ data accuracy for downstream users.
Extract and normalize unstructured web data into clean, analysis-ready formats (CSV, JSON, Google Sheets, BigQuery), applying parsing, regex and transformation rules to standardize fields like company names, addresses, phone numbers, and taxonomy tags.
Monitor and scrape social media channels, forums, review sites and news outlets to detect product mentions, brand sentiment, emerging competitor activity and industry trends, producing timely intelligence briefings for stakeholders.
Perform competitor and market mapping by compiling feature matrices, pricing comparisons, public roadmaps and funding information to create competitive intelligence reports and tactical recommendations.
Identify and qualify sales leads by combining web research, LinkedIn prospecting, corporate registries and public financial filings, enriching CRM records with firmographic and technographic data and noting source reliability.
Schedule and run automated data-collection jobs, including cron or cloud-based pipelines, and troubleshoot intermittent failures, CAPTCHA handling, rate-limiting, and site structure changes to maintain data continuity.
Integrate collected data into internal systems and pipelines (e.g., Salesforce, HubSpot, Airtable, Snowflake), coordinating with engineering and analytics teams to ensure seamless ingestion and mapping.
Carry out manual checks and quality assurance (QA) on sampled records, maintain error logs and implement corrective rules to reduce recurring data issues and improve dataset integrity over time.
Build and maintain searchable research indexes and source repositories (bookmarks, site lists, vendor contacts) and tag records with metadata to accelerate future investigations and team knowledge sharing.
Conduct ad-hoc research requests and deep-dive investigations for cross-functional partners (sales, product, legal, compliance), delivering concise findings, timelines, and recommended next steps.
Maintain compliance with data privacy laws (GDPR, CCPA), robots.txt constraints and terms of service by implementing consent-aware collection practices and escalation pathways for legal review when needed.
Document provenance and citation of web sources, noting capture dates, author credentials and archival URLs (e.g., Wayback Machine snapshots) to support reproducibility and audit readiness.
Coordinate with IT and security teams to securely store credentials, API keys and scrape outputs, enforce least-privilege access and follow organizational data retention and encryption policies.
Optimize research processes by evaluating and implementing third-party intelligence platforms, data enrichment services and automation tools to scale coverage and reduce manual effort.
Train and mentor junior researchers on search techniques, scraping best practices, data normalization rules and quality standards to build team capability and consistency.
Prepare and present periodic research summaries, dashboards and slide decks for stakeholders that highlight findings, methodology, confidence levels, limitations and recommended actions.
Track, triage and resolve data anomalies and stakeholder feedback, establishing SLA-driven response timelines and measuring impact of corrections on downstream reports and systems.
Conduct multilingual research and use translation tools or native-language resources when required to gather accurate records in non-English markets and verify cross-border corporate information.
Maintain awareness of industry developments in data collection, web technologies, anti-bot mitigations and legal guidelines to adapt methods and advise leadership on risk and opportunity.

Secondary Functions

Support ad-hoc analytical requests and exploratory data analysis to help product and business teams understand patterns and hypotheses surfaced by web data.
Contribute to the team's data strategy and roadmap by identifying recurring data needs, recommending new source coverage and estimating effort for automation vs. manual collection.
Collaborate with engineering and analytics teams to translate research requirements into data engineering tasks, prioritize backlog items and participate in sprint planning.
Create onboarding materials, research templates and internal knowledge base articles to standardize practices and accelerate new hire productivity.
Participate in cross-functional working groups to align data definitions, taxonomy and quality metrics across sales, marketing and analytics stakeholders.
Assist legal and compliance in responding to data subject requests or takedown notifications by locating and documenting scraped records and source URLs.
Evaluate vendor-supplied datasets and APIs for fit and quality, run pilot imports, and recommend procurement or integration based on cost-benefit analysis.
Maintain and update sample datasets and test environments for automated jobs to validate scraping changes before production deployment.
Help prepare RFP responses and commercial proposals by compiling market and competitor intelligence that supports positioning and pricing strategies.
Provide level 1 troubleshooting and support to internal users consuming web-research outputs, escalating technical issues as needed.

Required Skills & Competencies

Hard Skills (Technical)

Advanced internet research techniques: Boolean search, Google dorking, site:, inurl:, filetype:, cache:, and specialized search engine operators.
Web scraping and automation: hands-on experience with Python libraries (Scrapy, BeautifulSoup, Requests), Selenium, Puppeteer, or No-Code scraping platforms.
Data transformation and cleaning: regular expressions, pandas, OpenRefine, Excel advanced functions (VLOOKUP, INDEX/MATCH, pivot tables) and Google Sheets scripting.
API integration and JSON/XML parsing: experience pulling data from public and private APIs, handling pagination, rate limits and authentication (OAuth, API keys).
Data formatting and export: producing CSV, JSON, SQL-ready datasets and familiarity with cloud storage (AWS S3, Google Cloud Storage) and data warehouses (BigQuery, Redshift).
CRM and data systems: experience enriching and importing records into Salesforce, HubSpot or Airtable with proper deduplication and field mapping.
Quality assurance and versioning: implementing sampling methods, error tracking, and using Git or other version control for scripts and documentation.
Basic programming and automation: scripting in Python, JavaScript or R to automate repetitive tasks and pipeline orchestration.
Familiarity with anti-bot mitigation handling: CAPTCHA avoidance strategies (legal and compliant), rate limiting, header management and proxy usage.
Open-source intelligence (OSINT) tools: experience with LinkedIn Sales Navigator, WHOIS, company registries, SEC EDGAR, news aggregators and social listening tools.
Data privacy and compliance: working knowledge of GDPR, CCPA, robots.txt, and site terms of service implications for data collection.
Metadata management and taxonomy design: tagging, classification and consistent naming conventions for scalable datasets.
Basic SQL: querying data stores to validate imports, run joins and prepare datasets for stakeholders.
Multilingual research tools: using machine translation, local search engines and native-source repositories when researching non-English markets.

Soft Skills

Strong attention to detail and obsession with data accuracy and provenance.
Critical thinking and investigative mindset for triangulating disparate sources and identifying trustworthy information.
Excellent written communication for documenting methodology, limitations and presenting concise findings to non-technical stakeholders.
Time management and prioritization: managing multiple concurrent research requests and meeting SLA-driven deadlines.
Collaborative mindset: working cross-functionally with engineers, analysts, legal and product teams.
Problem-solving and adaptability in response to changing source structures, anti-bot defenses and emergent business needs.
Curiosity and continuous learning orientation to stay current on tools, frameworks and privacy regulations.
Customer-service focus for internal clients, including responsiveness, clear status updates and delivery of actionable outputs.
Ethical judgment and discretion when handling sensitive or personal data.
Teaching and mentoring capability to onboard junior researchers and share best practices.

Education & Experience

Educational Background

Minimum Education:

Associate degree or equivalent practical experience in Research, Information Science, Data Analytics, Computer Science, Business or related field.

Preferred Education:

Bachelor's degree in Information Science, Computer Science, Data Analytics, Market Research, Economics, Journalism, or a related discipline.

Relevant Fields of Study:

Information Science / Library Science
Computer Science / Software Engineering
Data Analytics / Statistics
Market Research / Business Intelligence
Journalism / Investigative Research

Experience Requirements

Typical Experience Range: 1–4 years of professional web research, scraping, data collection or intelligence work.

Preferred: 3+ years of experience executing automated and manual web research at scale, with demonstrated projects using scraping tools, API integrations, data cleaning pipelines, and delivering actionable intelligence or enriched datasets to business stakeholders.