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Data Enrichment Jobs (NOW HIRING)

NewRocket

AI Data Intelligence Lead

AI-powered data enrichment and inference * Entity resolution and fuzzy matching * Automated classification and taxonomy generation * Anomaly detection and data quality monitoring * Achema mapping and ...

NewRocket

AI Data Intelligence Lead

OR · Remote

AI-powered data enrichment and inference * Entity resolution and fuzzy matching * Automated classification and taxonomy generation * Anomaly detection and data quality monitoring * Achema mapping and ...

Trimble

Data/Analytics Engineer

Westminster, CO · On-site

$122K - $168K/yr

Data Enrichment and Insight Development * Spearhead data quality and completeness initiatives by designing advanced record linkage and enrichment pipelines using Python and the Splink library.

Brady

Customer Data Specialist

Salt Lake City, UT · Remote

$16.50 - $22/hr

Data Enrichment: Audit and fill missing data gaps by validating and integrating high-quality external data sources. Position Responsibilities: * Account Creation & Onboarding: Accurately set up new ...

Endurion

Data Scientist

Tampa, FL · On-site

Use data enrichment techniques to feed RDF triplestore and manage data with graph database. Uncovers and explains actionable insights from data by combining scientific method, math and statistics ...

Brady Corporation

Customer Data Specialist

Salt Lake City, UT · On-site

$16.50 - $22/hr

Data Enrichment: Audit and fill missing data gaps by validating and integrating high-quality external data sources. Position Responsibilities: * Account Creation & Onboarding: Accurately set up new ...

SOSi

Data Scientist III

Washington, DC · On-site

Overview SOSi is seeking a Data Scientist III to support cybersecurity data science and enrichment activities in alignment with our customer. This role is responsible for applying data science ...

SOSi

Data Scientist III

Washington, DC · On-site

Overview SOSi is seeking a Data Scientist III to support cybersecurity data science and enrichment activities in alignment with our customer. This role is responsible for applying data science ...

Clarium

AI Engineer - Data Intelligence

$150K - $180K/yr

You'll join the Data Products team, a small, unusually senior group responsible for the data assets ... Day-to-day, you'll build and own components of our enrichment pipeline: classification workflows ...

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Data Enrichment information

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How much do data enrichment jobs pay per hour?

As of Jul 13, 2026, the average hourly pay for data enrichment in the United States is $32.84, according to ZipRecruiter salary data. Most workers in this role earn between $18.03 and $36.54 per hour, depending on experience, location, and employer.

What is a data enrichment job?

A data enrichment job involves enhancing existing data by adding missing information, verifying accuracy, or updating records to improve data quality. It often requires skills in data analysis, familiarity with databases, and tools like CRM or data management software.

Which 3 jobs will survive AI?

Data Enrichment specialists will continue to be essential as AI tools often require human oversight for data accuracy and context. Roles involving complex judgment, creativity, and emotional intelligence—such as data analysts, data scientists, and AI trainers—are also likely to persist. These jobs benefit from skills in critical thinking, domain expertise, and familiarity with data management tools.

What are the typical daily tasks for someone working in Data Enrichment?

A typical day in Data Enrichment involves collecting, verifying, and updating information in company databases to ensure accuracy and completeness. You may use specialized software to cross-reference data sources, clean up existing records, and supplement missing fields with additional details from external or internal sources. Collaboration with sales, marketing, or data analytics teams is common, as enriched data often supports lead generation or business intelligence initiatives. This role requires a blend of independent focus and teamwork, as well as frequent communication to clarify requirements or resolve inconsistencies.

Is 40 too late for data science?

Data science is a field open to professionals of all ages, including those starting at 40 or later. Success depends on acquiring relevant skills such as programming, statistics, and tools like Python or R, as well as building a strong portfolio and gaining practical experience. Age is less important than your ability to learn and adapt to evolving technologies in the field.

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

To thrive in Data Enrichment, you need strong analytical skills, attention to detail, and experience working with large data sets, often supported by a degree in data science, information management, or related fields. Familiarity with data management tools such as Excel, SQL databases, CRM systems, and occasionally automation scripts or enrichment platforms is commonly required. Excellent organizational skills, critical thinking, and effective communication are valuable soft skills in this role. These capabilities ensure accurate, high-quality data improvement and seamless collaboration with broader data or marketing teams.

What jobs pay 500,000 a year?

In the field of data enrichment, high-paying roles such as senior data scientists, data engineers, or analytics directors can reach or exceed $500,000 annually, especially with extensive experience, advanced skills in data management tools, and leadership responsibilities. These positions often require advanced degrees, certifications, and a strong understanding of data infrastructure and business strategy.

What is a Data Enrichment job?

A Data Enrichment job involves improving and enhancing raw data by adding relevant information from various sources. This role typically includes verifying, cleaning, and supplementing data to ensure accuracy and completeness. Data enrichment professionals work with databases, CRM systems, and external data sources to provide valuable insights for businesses. Their efforts help organizations make better decisions, personalize customer interactions, and improve overall data quality.

More about Data Enrichment jobs
What cities are hiring for Data Enrichment jobs? Cities with the most Data Enrichment job openings:
What are the most commonly searched types of Data Enrichment jobs? The most popular types of Data Enrichment jobs are:
What states have the most Data Enrichment jobs? States with the most job openings for Data Enrichment jobs include:
What job categories do people searching Data Enrichment jobs look for? The top searched job categories for Data Enrichment jobs are:
Data Enrichment jobs near you
Infographic showing various Data Enrichment job openings in the United States as of July 2026, with employment types broken down into 79% Full Time, and 21% Part Time. Highlights an 84% In-person, 5% Hybrid, and 11% Remote job distribution, with an average salary of $68,307 per year, or $32.8 per hour.
Enterprise Data and AI Solutions Scientist

Enterprise Data and AI Solutions Scientist

SAIC

Washington, DC • On-site

Full-time

Posted 25 days ago

SAIC rating

7.9

Company rating: 7.9 out of 10

Based on 79 frontline employees who took The Breakroom Quiz

68th of 210 rated it services

Job description

Job Description
Description
We are seeking an Enterprise Data and AI Solutions Scientist to join our Hyperautomation team. This role is designed for an analytically curious and technically versatile "data hunter" who thrives when the required data source, system, field, or solution has not yet been identified.
The successful candidate will lead investigative data-discovery efforts across enterprise platforms, determine where relevant information resides, evaluate its reliability, correlate records across disparate systems, and translate findings into repeatable analytics, AI-enabled data-enrichment capabilities, and automated workflows.
This position goes beyond querying known datasets or producing predefined reports. It requires someone who can take an ambiguous business objective, investigate multiple enterprise systems, identify relationships among incomplete or conflicting datasets, and develop an evidence-based approach to solving the problem. This individual will bridge the gap between raw enterprise data, intelligent data enrichment, and automation.
What You Will Bring to the Team
You are more than a reporting specialist or traditional data scientist. You are comfortable beginning with an unanswered question, navigating unfamiliar enterprise systems, testing
This role is hybrid and reports onsite in Washington, DC at least 1 day a week and as required for meetings, testing or other gov activities as directed by their lead.
Key Responsibilities:
  • Investigative Data Discovery: Lead data-hunting and investigative analytics efforts in support of complex business, operational, security, and hyperautomation use cases.
  • Platform Exploration: Investigate enterprise platforms, particularly Splunk and ServiceNow, to identify relevant indexes, sourcetypes, tables, fields, APIs, relationships, and authoritative data sources.
  • Data Correlation and Reconciliation: Identify correlation keys across disparate systems, including configuration-management, endpoint, identity, asset, and operational data. Develop methods for reconciling incomplete, inconsistent, duplicated, or conflicting records.
  • Advanced Querying and Scripting: Develop and optimize searches, queries, scripts, and analytical workflows using SPL, SQL, Python, REST APIs, and related data-retrieval technologies.
  • AI-Driven Data Enrichment: Use approved AI and Generative AI capabilities, including prompt-based APIs, to normalize data, extract attributes, and generate missing data points from available record-level context. Examples may include using known IT asset manufacturers and models to determine or infer lifecycle attributes such as End of Life or End of Support.
  • AI Output Validation: Evaluate AI-generated attributes for accuracy, consistency, and business usability. Clearly distinguish authoritative source data from inferred or generated information and document supporting evidence, confidence, and known limitations.
  • Automation Integration: Partner with RPA, workflow, and data-engineering teams to convert successful analytical discoveries and enrichment processes into repeatable, governed, and sustainable enterprise capabilities.
  • Solution Design: Help determine whether a use case is best addressed through data engineering, API integration, business-process automation, robotic process automation, AI-enabled enrichment, or a hybrid approach.
  • Prototyping and Communication: Develop prototypes, proofs of concept, dashboards, and visualizations. Communicate findings, data limitations, technical risks, and recommendations to both technical teams and senior leadership.
Core Competencies:
  • Investigative Curiosity: A persistent drive to explore unfamiliar systems and data structures until a defensible answer or path forward is identified.
  • Systems Thinking: The ability to understand how data flows across applications, infrastructure, identity systems, assets, and business processes.
  • Evidence-Based Discipline: A rigorous approach to validating conclusions, documenting data lineage, and distinguishing authoritative, derived, and AI-generated information.
  • Solution Orientation: The ability to turn one-time discoveries into reusable, scalable, and supportable enterprise capabilities.
  • Consultative Communication: The ability to translate complex data findings into clear, actionable recommendations for technical and business stakeholders.
Illustrative Use Cases:
  • Correlating security, endpoint, identity, asset, and service-management data across Splunk, ServiceNow, and other platforms to identify vulnerable, unsupported, or untracked enterprise assets.
  • Sending known asset attributes, such as manufacturer, model, product family, and software version, to an approved AI service to generate missing lifecycle information, including estimated End-of-Life and End-of-Support dates.
  • Using AI to normalize inconsistent manufacturer names, product models, application titles, organizational values, and other records that cannot be reliably standardized through static rules alone.
  • Validating AI-generated data against available evidence, documenting the basis for each determination, and flagging low-confidence or conflicting results for human review.
  • Mapping a manual data-gathering and reconciliation process and redesigning it as an automated pipeline using Databricks, APIs, ServiceNow, Splunk, Power Automate, or RPA technologies.

Qualifications
Required Education & Experience:
  • Bachelor's degree in Data Science, Computer Science, Information Systems, Statistics, Engineering, or a related technical discipline and at least 2-5 years of relevant professional experience. Equivalent practical experience may be considered in lieu of a degree.
  • Demonstrated experience conducting data discovery or investigative analytics when the required data sources, fields, or technical approach were not predefined.
  • Hands-on Splunk experience, including SPL development, index and sourcetype discovery, field analysis, lookups, joins, and cross-source data correlation.
  • Hands-on experience navigating and querying ServiceNow data structures, including CMDB, asset, operational, or related enterprise tables and APIs.
  • Strong proficiency in SQL and Python for data retrieval, manipulation, integration, and analysis.
  • Experience working with REST APIs, JSON, and structured or semi-structured data.
  • Practical experience using AI, Generative AI, or prompt-based services to extract, classify, normalize, infer, or enrich enterprise data.
  • Experience evaluating and validating generated or inferred data before incorporating it into analytics, reporting, or operational processes.
  • Ability to work independently in ambiguous environments, formulate and test hypotheses, and adapt based on emerging findings.
  • Strong analytical, problem-solving, documentation, and technical communication skills.
Required Clearance:
  • US Citizenship.
  • Active Secret Clearance.
Preferred Qualifications:
  • Experience with Databricks, Apache Spark, Delta Lake, or cloud-based lakehouse architectures.
  • Experience integrating with AI or large language model services through APIs, including prompt design, structured outputs, response evaluation, and exception handling.
  • Experience developing or supporting workflows using Microsoft Power Automate or UiPath.
  • Experience operationalizing AI-generated or AI-enriched data through automated pipelines, dashboards, workflow tools, or human-in-the-loop review processes.
  • Familiarity with Retrieval-Augmented Generation, semantic matching, embedding models, vector databases, entity resolution, or related information-retrieval techniques.
  • Experience working in federal government, regulated-industry, cybersecurity, IT asset-management, or large-scale enterprise environments.

Target salary range: $80,001 - $120,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.
Overview
SAIC accepts applications on an ongoing basis and there is no deadline.
SAIC® is a premier mission integrator focused on advancing the power of technology and innovation to serve and protect our world. Our robust portfolio of offerings across the defense, space, intelligence, and civilian markets includes secure high-end solutions in mission IT, enterprise IT, engineering services, and professional services. We integrate emerging technology, rapidly and securely, into mission critical operations that modernize and enable critical national imperatives.
We are approximately 23,000 strong; driven by mission, united by purpose, and inspired by opportunities. SAIC is an Equal Opportunity Employer. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $7.3 billion. For more information, visit saic.com. For ongoing news, please visit our newsroom.

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