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Remote Viewing Jobs in Virginia (NOW HIRING)

... views-ensuring data accuracy, integrity, and trust across the enterprise. This role also requires ... This opportunity is 100% remote. Key Responsibilities Test Automation & QA Engineering * Design ...

This is a remote position. Essential Duties and Responsibilities: - Act as the entry point for new ... views-to ensure alignment with enterprise strategy and solution architecture. Use these artifacts ...

Senior Database Developer

Mclean, VA · Remote

$110K - $130K/yr

This is a full-time and remote position. Astor is looking for an experienced Oracle Database ... Implements PL/SL Programming especially in areas of Views, cursors, Stored Procedures ...

GEOINT Analyst Mid

Charlottesville, VA · Remote

$110K - $150K/yr

Provides non-literal analysis to produce intelligence products, using remote sensing methodologies ... viewer, such as ArcGIS or FalconView * TS/SCI clearance Global Comp $110,000- $150,000 This ...

This is a remote-friendly role based in Eastern time zone. The role involves assessing data sources ... SQL stored procedures, views, PBI data models) as foundational elements in the workspace. Design ...

Senior Software Developer

Herndon, VA · On-site +1

$56 - $74/hr

Senior Software Developer National Capital Region TS/SCI Polygraph | Hybrid (Remote with Occasional ... Write and optimize SQL queries, T-SQL stored procedures, views, and SSIS packages * Create ...

New

Data Engineer

Leesburg, VA · Remote

$115K - $139K/yr

This opportunity is 100% remote. The ideal candidate has hands-on experience with ETL/ELT pipelines ... Design and implement materialized views and other performance optimization techniques to improve ...

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Showing results 1-20

Remote Viewing information

See Virginia salary details

$16

$29

$59

How much do remote viewing jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for remote viewing in Virginia is $29.68, according to ZipRecruiter salary data. Most workers in this role earn between $19.09 and $23.85 per hour, depending on experience, location, and employer.

What is a Remote Viewing job?

A Remote Viewing job typically involves using structured techniques to gather information about distant or unseen targets without direct physical access. This practice is often associated with psychic or intuitive abilities, but some organizations use it in research or investigative contexts. Remote viewers may be employed in fields such as paranormal research, security consulting, or private investigation. While the legitimacy of remote viewing remains debated, some individuals claim success in providing useful insights.

What are the most common day-to-day responsibilities in a Remote Viewing role?

In a Remote Viewing position, your main daily tasks typically involve monitoring live video feeds or surveillance footage to detect and report unusual activity, security breaches, or other incidents. You may also be responsible for creating detailed reports, maintaining logs, and communicating effectively with on-site personnel or local authorities when necessary. Depending on the employer, you might work independently or as part of a larger security team and could be assigned to oversee multiple locations simultaneously. Staying alert and attentive throughout your shift is crucial to ensuring timely responses to any events that arise.

What are the key skills and qualifications needed to thrive in the Remote Viewing position, and why are they important?

To thrive in a Remote Viewing job, candidates typically need strong observational skills, attention to detail, and the ability to accurately document and communicate findings, with at least a high school diploma or equivalent required. Experience with surveillance or monitoring software, video analytics tools, and familiarity with standard operating procedures is often essential. Strong reliability, professionalism, and the ability to stay focused during long periods of monitoring make candidates stand out. These attributes are important for ensuring accurate reporting, preventing incidents, and maintaining safety or compliance in security-related environments.

How to make $1000 a week remotely?

Remote viewing as a job typically involves offering psychic or intuitive services, which can generate income through client sessions, online platforms, or coaching. Earning $1000 weekly requires building a client base, marketing skills, and consistent availability, often supplemented by related skills like meditation or visualization. Success depends on reputation, pricing, and demand for remote viewing services.

What are remote viewing jobs?

Remote viewing jobs involve using mental perception techniques to gather information about distant or unseen targets, often for research or intelligence purposes. These roles may require training in specific protocols, strong visualization skills, and the ability to work independently in a virtual environment.

Can I become a remote viewer?

Remote viewing is a skill that can be developed through training and practice, often involving mental focus and visualization techniques. Some organizations offer courses or certifications, but it is not a formally recognized profession with standardized qualifications. Success in remote viewing depends on individual aptitude and dedication.

How much do remote viewers get paid?

Remote viewers' pay varies widely depending on experience, client, and project scope, with some earning hourly rates from $20 to $100 or more. Many remote viewing jobs are freelance or contract-based, and income can fluctuate based on the number of assignments completed and the reputation of the viewer.
What are popular job titles related to Remote Viewing jobs in Virginia? For Remote Viewing jobs in Virginia, the most frequently searched job titles are:
What cities in Virginia are hiring for Remote Viewing jobs? Cities in Virginia with the most Remote Viewing job openings:
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA • On-site, Remote

Full-time

Re-posted 19 days ago


Job description

Anika Systems is seeking a highly technical Quality Assurance Engineer with strong development, SQL, and Python expertise to support enterprise data platforms for federal clients. This is not a traditional manual QA role and this position requires a developer mindset, focused on automation, data validation, and platform reliability across modern cloud-based architectures.
The ideal candidate will design and implement automated testing frameworks for ETL pipelines, Apache Iceberg data architectures, XBRL datasets, and performance-optimized structures such as materialized views-ensuring data accuracy, integrity, and trust across the enterprise. This role also requires proficiency in AI tools and AI-driven workflows, leveraging automation and intelligent testing techniques to improve quality and delivery speed.
This opportunity is 100% remote.
Key Responsibilities
Test Automation & QA Engineering
  • Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL.
  • Build reusable testing utilities for data validation, regression testing, and pipeline certification.
  • Integrate automated tests into CI/CD pipelines to support continuous testing and deployment.
  • Develop unit, integration, and end-to-end test cases for complex data workflows.
  • Leverage AI-assisted testing tools to generate test cases, identify edge cases, and improve test coverage.
Data Validation & ETL Testing
  • Validate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of data.
  • Create automated checks for data completeness, consistency, accuracy, and timeliness.
  • Test ingestion and transformation of complex datasets, including XBRL financial data.
  • Implement reconciliation and audit mechanisms across source-to-target mappings.
  • Apply AI-driven anomaly detection to identify data quality issues and pipeline failures.
Iceberg & Materialized View Testing
  • Develop and execute test strategies for Apache Iceberg-based data lakehouse architectures, including:
    • Schema evolution validation
    • Time travel and versioning accuracy
    • Partitioning and performance behavior
  • Validate and compare materialized views vs. Iceberg table performance and consistency, including:
    • Query performance benchmarking
    • Data freshness and latency
    • Storage efficiency and maintenance overhead
  • Ensure alignment between precomputed datasets (materialized views) and underlying source data.
Data Quality, Metadata & Context Validation
  • Implement automated validation for data quality rules, lineage, and metadata accuracy.
  • Support context engineering by validating that datasets include proper business context, definitions, and relationships.
  • Integrate QA processes with enterprise data catalogs and metadata systems to ensure discoverability and trust.
  • Validate AI-generated metadata, lineage, and transformations for accuracy and traceability.
AI-Driven Quality Engineering
  • Apply AI/ML and generative AI tools to enhance QA processes, including intelligent test generation, defect prediction, and automated root cause analysis.
  • Validate data readiness for AI/ML and generative AI use cases, ensuring datasets meet quality, completeness, and governance standards.
  • Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.
  • Ensure alignment with responsible AI practices, including traceability, explainability, and data integrity.
OCDO & Data Strategy Support
  • Support enterprise data management programs and OCDO initiatives by ensuring data quality and reliability across systems.
  • Contribute to data maturity assessments by evaluating data quality, testing coverage, and governance adherence.
  • Align QA processes with Federal Data Strategy and Evidence Act requirements.
Stakeholder Collaboration & Agile Delivery
  • Work closely with data engineers, data architects, and analysts to define test strategies and acceptance criteria.
  • Participate in stakeholder engagement sessions and listening campaigns to understand data quality expectations and pain points.
  • Document test results, defects, and quality metrics for both technical and non-technical stakeholders.
  • Operate within Agile teams to iteratively improve data quality processes and tooling.
  • Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows.
Required Qualifications
  • Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
  • 5+ years of experience in QA engineering, data testing, or software development.
  • Strong programming skills in Python and advanced proficiency in SQL.
  • Experience building automated test frameworks for data platforms and ETL pipelines.
  • Hands-on experience with:
    • AWS data services (S3, Glue, Redshift, Lambda, etc.)
    • Apache Iceberg or similar data lake technologies
  • Experience validating materialized views and performance-optimized data structures.
  • Familiarity with XBRL or complex financial/regulatory datasets.
  • Understanding of data modeling, metadata, and data governance principles.
  • Experience with CI/CD tools and automated testing integration.
  • Demonstrated proficiency with AI tools and AI-assisted development/testing workflows.
  • Understanding of data quality requirements for AI/ML and analytics use cases.
  • U.S. Citizenship required; ability to obtain and maintain a federal clearance.
Preferred Qualifications
  • Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve System.
  • Familiarity with data catalog and governance tools (e.g., Collibra, Alation, ServiceNow).
  • Experience with Apache Spark or distributed data processing frameworks.
  • Knowledge of data quality tools and observability platforms.
  • Exposure to data maturity frameworks (e.g., EDM DCAM, TDWI).
  • Experience testing large-scale cloud data platforms and lakehouse architectures.
  • Experience validating data pipelines supporting AI/ML, analytics, or generative AI solutions.
  • Familiarity with AI-driven testing tools or frameworks.