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Data Operations Analyst Jobs (NOW HIRING)

Allocate

Senior Data Operations Analyst

New York, NY · On-site

$95K - $135K/yr

About the Role Allocate is looking to add a Senior Data Operations Analyst to the team in New York! There's a lot for us to build, and we need a sharp, detail-oriented operator who can jump right in ...

New

Tempus

Sr. Data Operations Analyst

Chicago, IL · On-site

$88K - $111K/yr

As a Diagnostic Data Operations Analyst, you will serve as the technical data backbone of Tempus's diagnostics business unit. This role operates cross-functionally across Strategy & Operations ...

Tempus

Sr. Data Operations Analyst

Chicago, IL · On-site

$88K - $111K/yr

As a Diagnostic Data Operations Analyst, you will serve as the technical data backbone of Tempus's diagnostics business unit. This role operates cross-functionally across Strategy & Operations ...

Carta

Client Data Operations Analyst

Sandy, UT · On-site

The Problems You'll Solve As a Data Ops Analyst, you will be responsible for the lifecycle of ... Keep detailed documentation of client interactions to identify trends and drive operational and ...

New

Carta

Client Data Operations Analyst

Sandy, UT · On-site

The Problems You'll Solve As a Data Ops Analyst, you will be responsible for the lifecycle of ... Keep detailed documentation of client interactions to identify trends and drive operational and ...

New

Carta

Client Data Operations Analyst

Seattle, WA · On-site

$36.88 - $43.39/hr

The Problems You'll Solve As a Data Ops Analyst, you will be responsible for the lifecycle of ... Keep detailed documentation of client interactions to identify trends and drive operational and ...

New

Carta

Client Data Operations Analyst

Seattle, WA · On-site

$36.88 - $43.39/hr

The Problems You'll Solve As a Data Ops Analyst, you will be responsible for the lifecycle of ... Keep detailed documentation of client interactions to identify trends and drive operational and ...

New

UBS

Financial Data Operations Analyst

Chicago, IL · On-site

$70K - $80K/yr

... with operational requirements. • perform business analysis and define requirements for data management processes and system workflows. • support user acceptance testing (UAT) and production ...

New

UBS

Financial Data Operations Analyst

Chicago, IL · On-site

$80K - $100K/yr

... with operational requirements. • perform business analysis and define requirements for data management processes and system workflows. • support user acceptance testing (UAT) and production ...

New

Ercot

Data Operations Analyst Sr

Taylor, TX · Hybrid

$99K - $136K/yr

Lead incident response and root-cause analysis for critical data operations issues, driving permanent fixes. * Architect monitoring and observability frameworks for data pipelines, BI refreshes, and ...

New

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All JobsData Operations Analyst Jobs

Data Operations Analyst information

See salary details

$34K

$82.6K

$136K

How much do data operations analyst jobs pay per year?

As of Jul 14, 2026, the average yearly pay for data operations analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What are Data Operations Analysts?

Data Operations Analysts are professionals who manage, optimize, and ensure the accuracy of data workflows within an organization. They are responsible for collecting, processing, and analyzing data to support business operations and decision-making. Their duties often include maintaining databases, troubleshooting data issues, and collaborating with other teams to improve data quality and efficiency. By ensuring data integrity and availability, Data Operations Analysts help organizations make data-driven decisions and streamline their operations.

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

To thrive as a Data Operations Analyst, you need strong analytical skills, proficiency in data management, and a relevant degree such as in computer science, statistics, or a related field. Familiarity with SQL, data visualization tools (such as Tableau or Power BI), and data pipeline systems is typically required, along with certifications like Microsoft Certified: Data Analyst Associate being advantageous. Attention to detail, problem-solving ability, and effective communication are essential soft skills for collaborating with cross-functional teams and ensuring data integrity. These skills and qualities are vital for maintaining accurate data flows, supporting business decisions, and driving operational efficiency.

How does a Data Operations Analyst typically collaborate with other departments to improve data processes?

Data Operations Analysts regularly work with cross-functional teams, including IT, business intelligence, and department leads, to streamline data collection, integration, and reporting. They often serve as the bridge between technical and non-technical teams, translating business needs into actionable data solutions. Effective collaboration may involve participating in meetings to understand project requirements, troubleshooting data issues with engineering teams, and training staff on new data tools or procedures. Strong communication skills are essential, as the role requires aligning diverse stakeholders toward common data integrity goals.

What is the difference between Data Operations Analyst vs Data Analyst?

AspectData Operations AnalystData Analyst
Required CredentialsBachelor's in Data Science, IT, or related field; certifications like Microsoft Certified Data AnalystBachelor's in Statistics, Mathematics, or related field; certifications like Microsoft Certified Data Analyst
Work EnvironmentData teams, IT departments, business operationsBusiness units, marketing, finance, or research teams
Employer & Industry UsageTech companies, finance, healthcare, retailMarketing agencies, consulting firms, finance, healthcare

While both roles involve working with data, Data Operations Analysts focus on managing data workflows, ensuring data quality, and supporting data infrastructure. Data Analysts primarily analyze data to generate insights, reports, and support decision-making. The roles often overlap but differ in their core responsibilities and focus areas.

More about Data Operations Analyst jobs
What cities are hiring for Data Operations Analyst jobs? Cities with the most Data Operations Analyst job openings:
Who are the top companies hiring for Data Operations Analyst jobs? The top employers for Data Operations Analyst jobs are:
What states have the most Data Operations Analyst jobs? States with the most job openings for Data Operations Analyst jobs include:
What job categories do people searching Data Operations Analyst jobs look for? The top searched job categories for Data Operations Analyst jobs are:
Data Operations Analyst jobs near you
Infographic showing various Data Operations Analyst job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 1% Internship, 86% Full Time, 6% Part Time, 1% Temporary, and 5% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $82,640 per year, or $39.7 per hour.

Product Data Operations Analyst

NEFCO Construction Supply LLC

East Hartford, CT • On-site

$65K - $75K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Description

NEFCO is scaling from $1B to $5B, and high-quality product data is foundational to that growth. We are looking for a detail-oriented, technically curious Product Data Operations Analyst to maintain, enrich, and improve product data quality across ERP, PIM, supplier, and analytics systems - while identifying opportunities to automate workflows and strengthen data governance.


What You'll Do 

  • Maintain and enrich product data attributes - descriptions, specs, classifications, images, and identifiers (UNSPSC, GTIN, MPN) - across ERP and PIM systems.
  • Source missing attributes from suppliers, manufacturers, and third-party databases.
  • Support new product onboarding: review, validate, and enrich data before loading into downstream systems.
  • Apply data quality standards and run regular audits to catch gaps, inconsistencies, and duplicates.
  • Build and maintain dashboards tracking data quality KPIs, completeness scores, and enrichment progress.
  • Use SQL, Excel, Power Query, ETL tools, and AI-assisted scripting to automate data prep and validation workflows.
  • Partner with Pricing, Sales, Supply Chain, IT, and Operations to translate business needs into data requirements.
  • Support ERP/PIM improvement initiatives; assist with supplier portals, catalog syndication, and compliance projects.
  • Train end-users on data best practices and self-service tools.




Requirements

Qualifications

  •  Bachelor's degree in business, Information Systems, Data Analytics, or related field (or equivalent experience).
  • 2-4 years in product data, master data, PIM/ERP data management, catalog operations, or a related analytics role.
  • Solid SQL skills and strong Excel proficiency (pivot tables, Power Query, lookups, data validation).
  • Working knowledge of ETL concepts and comfort with scripting, automation, or AI-assisted data tools.
  • Experience with ERP or PIM systems; Epicor Eclipse or Salsify a plus.
  • Detail-oriented, organized, and able to manage competing priorities.
  • Clear communicator with both technical and non-technical stakeholders.
  • Power BI, Tableau, or similar BI tools.
  • Python for data manipulation and automation.
  • Experience with ETL pipelines, APIs, or FTP/SFTP data exchanges.
  • Background in industrial distribution, manufacturing, or wholesale.
  • Exposure to AI-assisted enrichment tools or LLMs for classification and content generation.
  • Familiarity with data governance, taxonomy management, or product classification.


What Success Looks Like

  • Product data is more complete, accurate, and easier for teams to use.
  • Data quality issues are discovered early and resolved through repeatable processes.
  • Manual cleanup is accomplished through automation, ETL workflows, and better source collection.
  • Product onboarding is consistent and less reliant on tribal knowledge.
  • Stakeholders trust the data because standards and ownership are clearly defined.

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