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Data Retrieval Jobs (NOW HIRING)

VTG

Data Engineer

Chantilly, VA · On-site

$118K - $142K/yr

Develop and optimize SQL queries to support data retrieval, transformation, and analysis. * Work with relational databases such as PostgreSQL, Oracle, and MySQL to store, manage, and access data.

The Atlantic Group

Data Engineer

Greenwich, CT · On-site

$128K - $154K/yr

Write optimized SQL queries and stored procedures for data retrieval and analysis; * Manage policies and processes for data governance and data quality; * Create and maintain documentation for data ...

Visa

Data Engineer

Bellevue, WA · Hybrid

$129K - $155K/yr

You will work on AI-native data systems including retrieval infrastructure, vector indexing, semantic knowledge platforms, real-time context pipelines, orchestration data flows, and intelligent data ...

Round Rock

Data Entry Operator

Round Rock, TX · On-site

$25 - $30/hr

Generate routine reports and assist in data retrieval as requested. * Adhere to data privacy laws and hospital confidentiality standards at all times. * Perform regular backups to ensure data ...

Talkishco

Data Entry Clerk

Charlotte, NC

Organize and maintain files for quick and easy data retrieval. * Identify and correct errors in data entries, ensuring integrity across all records. * Generate reports as requested by various ...

Staffingine LLC

Drone Data Engineer

Houston, TX · On-site

$109K - $131K/yr

Flight data retrieval * Media videoimage ingestion * Telemetry and metadata extraction * Integrate Skydio drone data with * Enterprise data platforms * Asset management systems * GIS systems * AIML ...

ENNOVARA

Data Analyst

Las Vegas, NV · On-site

$25 - $35/hr

... retrieval, visualization, and investigation with minimal turn-around time. o Develop processes for standardizing raw data coming from different sources in a variety of formats, and support team ...

Northwest Austin

Data Entry Operator

Austin, TX

$25 - $30/hr

Generate routine reports and assist in data retrieval as requested. * Adhere to data privacy laws and hospital confidentiality standards at all times. * Perform regular backups to ensure data ...

North Austin

Data Entry Operator

Austin, TX · On-site

$25 - $30/hr

Generate routine reports and assist in data retrieval as requested. * Adhere to data privacy laws and hospital confidentiality standards at all times. * Perform regular backups to ensure data ...

ENNOVARA

Data Analyst

Las Vegas, NV · On-site

$25 - $35/hr

... retrieval, visualization, and investigation with minimal turn-around time. o Develop processes for standardizing raw data coming from different sources in a variety of formats, and support team ...

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How much do data retrieval jobs pay per hour?

As of Jun 19, 2026, the average hourly pay for data retrieval in the United States is $25.82, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $25.24 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Retrieval Specialist, and why are they important?

To thrive as a Data Retrieval Specialist, a strong background in database management, data analysis, and information systems—often supported by a relevant degree—is essential. Familiarity with SQL, data extraction tools, and enterprise database systems such as Oracle or Microsoft SQL Server is typically required. Attention to detail, problem-solving abilities, and clear communication skills help professionals interpret requirements and ensure data accuracy. These competencies are crucial for efficiently locating, extracting, and validating data to support business decision-making and compliance.

What jobs make $1,000,000 a year?

In the field of data retrieval, high-earning roles such as chief data officers, data science executives, or senior data consultants can reach or exceed $1 million annually, especially in large corporations or tech firms. These positions often require extensive experience, advanced skills in data management and analytics, and leadership responsibilities. Compensation at this level may include base salary, bonuses, stock options, and other incentives.

What is data retrieval?

Data retrieval is the process of obtaining and extracting specific information from a database, storage system, or other data sources. It involves using queries or search techniques to locate and access relevant data efficiently and accurately. Data retrieval is essential in fields like data analysis, information management, and business intelligence, as it helps organizations make informed decisions based on up-to-date information. Professionals in this area often use tools and programming languages such as SQL, Python, or specialized data management software to streamline the retrieval process.

What is the difference between Data Retrieval vs Data Analyst?

AspectData RetrievalData Analyst
Primary RoleExtracting data from databases or sourcesInterpreting, analyzing, and visualizing data
Skills & CertificationsSQL, database managementSQL, statistics, data visualization tools
Work EnvironmentDatabase systems, data warehousesAnalytics platforms, reporting tools
Industry UsageIT, database management, data engineeringBusiness intelligence, marketing, finance

Data Retrieval focuses on extracting data efficiently from sources, while Data Analysts interpret and analyze that data to support decision-making. Both roles often collaborate but serve different functions within data management and analysis processes.

How to get a job in data recovery?

To get a job in data recovery, candidates typically need a background in computer science, information technology, or a related field, along with skills in data storage devices, file systems, and troubleshooting. Certifications such as CompTIA A+ or specialized training in data recovery tools can improve employability. Experience with hardware repair, data recovery software, and understanding of data security are also valuable for this role.

What are some common challenges faced in a Data Retrieval role and how can they be addressed?

Professionals in Data Retrieval often encounter challenges such as dealing with large, unstructured datasets, ensuring data accuracy, and maintaining data security. Addressing these issues typically requires proficiency with advanced query languages, data cleaning tools, and strong attention to detail. Collaborating closely with data engineers and analysts can also help in developing efficient retrieval processes and verifying data integrity. Continuous learning and staying updated with the latest tools and best practices are crucial for overcoming these challenges effectively.

What job makes $10,000 a month without a degree?

A data retrieval specialist or similar roles in data analysis and information management can potentially earn $10,000 or more per month through freelance work, consulting, or high-demand positions that require strong technical skills and experience. These roles often involve working with databases, data mining tools, and programming languages like SQL or Python, and may not require a formal degree but do demand expertise and proven ability to deliver results.

Is 40 too late for data science?

Data retrieval roles and data science careers do not have strict age limits; many professionals transition into these fields later in life. Success depends on acquiring relevant skills such as programming, statistics, and tools like SQL or Python, regardless of age. Continuous learning and practical experience are key factors for career advancement in data-related jobs.
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What job categories do people searching Data Retrieval jobs look for? The top searched job categories for Data Retrieval jobs are:
Data Retrieval jobs near you
Infographic showing various Data Retrieval job openings in the United States as of June 2026, with employment types broken down into 6% As Needed, 13% Full Time, and 81% Part Time. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $53,700 per year, or $25.8 per hour.
Senior Data Engineer - AI Context & Knowledge Systems

Senior Data Engineer - AI Context & Knowledge Systems

Everest Technologies, Inc.

Remote

$117K - $140K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

We are looking for a Data Engineer to build the "memory" and "knowledge" backbone of our Agentic AI ecosystem. You will be responsible for designing data pipelines that feed into our Model Context Protocol (MCP) servers, ensuring that AI agents managed via Gravitee have real-time access to accurate, secure, and contextually relevant enterprise data.
Key Responsibilities
  • Context Engineering: Design and optimize data schemas specifically for LLM consumption, ensuring that data retrieved via MCP servers is structured to minimize token usage and maximize reasoning accuracy.
  • Hybrid Pipeline Development: Build robust data pipelines using Python (for AI/ML workflows) and C#/.NET (for enterprise integration) to move data from legacy systems into AI-ready formats.
  • Vector Database Management: Implement and maintain Vector Databases (e.g., Pinecone, Weaviate, or Milvus) to support Retrieval-Augmented Generation (RAG) alongside live API tool calls.
  • Data Governance for AI: Work with the Gravitee API Gateway to enforce data masking, PII redaction, and fine-grained access control before data reaches an LLM.
  • Metadata Orchestration: Manage the OpenAPI and MCP metadata that allows AI agents to "understand" the data they are querying.
Technical Qualifications
  • Languages: Expert-level Python (Pandas, PySpark, SQLAlchemy) and strong familiarity with C# for interacting with .NET-based data layers.
  • AI Data Stack: Hands-on experience with Vector Databases and embedding models.
  • API Management: Understanding of how data is exposed through Gravitee APIM and secured via MCP-specific authorization flows.
  • Modern Data Stack: Experience with SQL/NoSQL databases, dbt, and cloud data warehouses (Snowflake, BigQuery, or Databricks).
  • Protocol Knowledge: Familiarity with the Model Context Protocol (MCP) and how it standardizes data retrieval for AI agents.
Preferred Skills
  • Experience building Knowledge Graphs to provide relational context to AI agents.
  • Familiarity with semantic caching to reduce LLM costs and improve response times.
  • Knowledge of Gravitee Observability for monitoring data drift in agentic conversations.

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