Data Quality Analyst Jobs for OPT Students
Data Quality Analyst roles are a strong fit for F-1 OPT students with backgrounds in statistics, computer science, or information systems. Most positions qualify as STEM OPT extensions, giving you up to 36 months of work authorization. Employers in finance, healthcare, and tech actively hire for these roles year-round.
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INTRODUCTION
We Breathe Life Into Data. At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That’s why we built the Healthcare Map — the industry’s largest, most complete, precise view of the U.S. healthcare system — by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare’s most complex questions for our partners. Across the healthcare ecosystem, we’re helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease. As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver “wow,” and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease — and enjoy the journey along the way.
THE OPPORTUNITY AT KOMODO HEALTH
Quality is core to Komodo Health’s mission to reduce the global burden of disease. As we scale the Healthcare Map® and AI-driven products, the need for independent, rigorous data quality oversight is critical. We’re not hiring a traditional QA or testing specialist. We’re looking for an analytically rigorous, detail-oriented professional who knows pipelines can run flawlessly yet produce analytically wrong data—and that meeting specs doesn’t always mean meeting customer needs. This is where the Senior Data Quality Analyst comes in. You’ll be the independent voice on data outputs—asking not just “did it run?” but “does this make sense for the customer?” Grounded in how customers use healthcare data, you’ll drive the analysis and quality monitoring needed to ensure it delivers real value.
ROLE MISSION & MANDATE
Responsibilities
The Senior Data Quality Analyst will own three standing operational responsibilities that are critical to Komodo’s weekly data delivery and major version release process:
- Data output validation: Run pre/post-release comparisons across key attributes to ensure changes meet data quality standards—not just engineering specs.
- Bug investigation support: Investigate issues from customer complaints and monitoring, document findings, and partner with engineering on root cause and resolution.
- Weekly publication review preparation: Assemble the execution summary, test coverage audit, and issue disposition list to support DPQ’s Monday release recommendation.
WHY THIS ROLE, WHY NOW
Komodo’s Data Product org is evolving—formalizing quality ownership, embedding automated QA, and establishing DPQ as the independent voice for every release. You’re joining at the moment that authority is being defined and put into practice.
LOOKING BACK ON YOUR FIRST 12 MONTHS AT KOMODO HEALTH, YOU WILL HAVE…
I. DATA OUTPUT VALIDATION (PRIMARY FOCUS)
The core of this role is independently assessing whether Komodo’s data outputs are analytically sound—ensuring the data tells the right story for customers, not just that the engineering works.
- Release validation: Design and run pre/post-release comparisons across key attributes (patient counts, claim volumes, fill rates, deduplication, payer attribution, provider coverage).
- Anomaly identification: Surface and document issues missed by automated tests—valid but suspicious patterns like demographic shifts, volume changes, or rule edge cases.
- Disposition recommendations: For each issue, assess what changed, customer impact, and recommend action (approve, conditional approve, hold, or escalate).
- Coverage documentation: Track what was tested, what passed, and accepted risks for each release—creating an auditable quality trail.
II. BUG INVESTIGATION & ROOT CAUSE ANALYSIS
Data quality issues rarely surface clearly—this role requires the rigor to navigate ambiguity and the precision to communicate findings to both technical and non-technical stakeholders.
- Issue triage: Review and prioritize DPQ Jira issues, distinguishing data output problems (DPQ), pipeline failures (engineering), and cases needing joint investigation.
- Hands-on investigation: Query Snowflake to trace anomalies to source, validate against expectations, and rule out alternatives.
- Findings documentation: Produce clear reports outlining the issue, evidence, likely cause, and next steps for both technical and non-technical audiences.
- Resolution coordination: Partner with Data Engineering and Architects to drive resolution and verify fixes address the root issue.
III. WEEKLY PUBLICATION REVIEW PREPARATION
Each week, Komodo publishes updated data. DPQ owns the release recommendation at the Monday meeting, and this role assembles the inputs that inform that decision.
- Pipeline execution summary: Compile a weekly record of which data pipelines ran, their completion status, any anomalies in run time or output volume, and a comparison against expected behavior.
- Test coverage audit: For each pipeline that ran during the week, document which quality checks were expected to execute and which did, surfacing any gaps in coverage that require manual review or escalation.
- Issue consolidation: Aggregate all quality issues raised during the week — from automated alerts, manual testing, and customer reports — into a single structured view with status, severity, and recommended disposition.
- Release recommendation package: Prepare the DPQ release recommendation document in advance of the Monday meeting, enabling DPQ leadership to review and present a confident, evidence-based recommendation.
WHAT YOU BRING TO KOMODO HEALTH
This role rewards people who are energized by the gap between “it ran as designed” and “it’s actually right.” The ideal candidate is:
- Intellectually curious: Driven to investigate the unknown in complex data—never taking outputs at face value and always digging for root cause.
- Analytically rigorous: Go beyond error checks—spot missing data, unexpected trends, and subtle signals that something’s off.
- Customer-aware: Define quality by whether customers can answer real healthcare questions—not just whether tests pass.
- Precise communicator: Write clear, actionable reports for engineers and accessible summaries for non-technical partners.
- Operationally reliable: Consistently deliver a complete, on-time release package for the weekly publication meeting.
TECHNICAL SKILLS & EXPERIENCE
- Experience: 4+ years of experience in data quality, data analysis, or analytics engineering — preferably in healthcare, life sciences, or another domain with complex, multi-source data.
- SQL proficiency: Strong SQL skills for large-scale analysis—joins, window functions, aggregations, and tracing data lineage. Snowflake preferred.
- Data investigation: Proven ability to work through ambiguous issues from signal to root cause—ruling out as well as ruling in.
- Structured QA process: Experience designing or executing pre/post release testing, including defining attributes, tolerances, and escalation criteria.
- Python (preferred): Comfortable using Python for analysis, validation, and light automation—able to read and adapt existing scripts.
- Healthcare data (required): Familiarity with claims data (medical, pharmacy, enrollment) and common quality patterns and failure modes.
EXPECTATIONS OF AI USE IN THIS ROLE (REQUIRED)
Ability to leverage AI tools (Gemini, Claude, Cursor, etc.) to enhance personal productivity, streamline workflows, content and visualization creation.
WHAT THIS ROLE IS NOT
We want candidates to be clear-eyed about the scope of this role:
- This is not a data engineering role—you won’t build or maintain pipelines.
- This is not a software QA role—you won’t write unit tests, manage CI/CD, or review code.
- This is not a pure reporting role — you will be doing hands-on analytical work with real data, not summarizing dashboards others built.
- This is a data-first role where analytical judgment and quality intuition matter as much as execution.
The pay range for each job posting reflects a minimum and maximum range of annual base pay that we reasonably expect to pay for this position within the US. We carefully consider multiple business-related factors when determining compensation, including job-related skills, work experience, geographic work location, relevant training and certifications, business needs and market demands. The starting annual base pay for this role is listed below. This position may be eligible for performance-based bonuses as determined in the Company’s sole discretion and in accordance with a written agreement or plan. This role may also be eligible for equity awards. In addition, this role is eligible for benefits including, but not limited to, comprehensive health, dental, and vision insurance; flexible time off and holidays; 401(k) with company match; disability insurance and life insurance; and leaves of absence in accordance with applicable state and local laws and regulations and company policy.
COMPENSATION
- San Francisco Bay Area And New York City: $147,000—$199,000 USD
- All Other US Locations: $128,000—$173,000 USD
KOMODO'S AI STANDARD
At Komodo, we're not just witnessing the AI revolution – we're leading it. This is a pivotal moment in time, where being first to market with AI transforms industries and sets the bar. We've already established industry leadership in leveraging AI to revolutionize healthcare, and we expect every team member to contribute. AI here isn't optional; it's foundational. We expect you to integrate AI into your daily work – from summarizing documents to automating workflows and uncovering insights. This isn't just about efficiency; it's about making every moment more meaningful, building on trust in AI, and driving our collective success.
WHERE YOU’LL WORK
Komodo Health has a hybrid work model with hubs in San Francisco, New York City, and Chicago. Roles vary — some can be performed from anywhere in the country, others are scoped to a specific region, and some are based near one of our hubs. For hub-based Dragons, we're building intentional in-office rhythms alongside the flexibility that's core to how we work. Whatever your setup, expectations will always be clear before you join.
EQUAL OPPORTUNITY STATEMENT
Komodo Health provides equal employment opportunities to all applicants and employees. We prohibit discrimination and harassment of any type with regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. By submitting your application, you acknowledge that you have read and understand Komodo Health’s Privacy Notice for Employees and Contractors. This notice explains how we collect, use, and retain applicant data.

INTRODUCTION
We Breathe Life Into Data. At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That’s why we built the Healthcare Map — the industry’s largest, most complete, precise view of the U.S. healthcare system — by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare’s most complex questions for our partners. Across the healthcare ecosystem, we’re helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease. As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver “wow,” and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease — and enjoy the journey along the way.
THE OPPORTUNITY AT KOMODO HEALTH
Quality is core to Komodo Health’s mission to reduce the global burden of disease. As we scale the Healthcare Map® and AI-driven products, the need for independent, rigorous data quality oversight is critical. We’re not hiring a traditional QA or testing specialist. We’re looking for an analytically rigorous, detail-oriented professional who knows pipelines can run flawlessly yet produce analytically wrong data—and that meeting specs doesn’t always mean meeting customer needs. This is where the Senior Data Quality Analyst comes in. You’ll be the independent voice on data outputs—asking not just “did it run?” but “does this make sense for the customer?” Grounded in how customers use healthcare data, you’ll drive the analysis and quality monitoring needed to ensure it delivers real value.
ROLE MISSION & MANDATE
Responsibilities
The Senior Data Quality Analyst will own three standing operational responsibilities that are critical to Komodo’s weekly data delivery and major version release process:
- Data output validation: Run pre/post-release comparisons across key attributes to ensure changes meet data quality standards—not just engineering specs.
- Bug investigation support: Investigate issues from customer complaints and monitoring, document findings, and partner with engineering on root cause and resolution.
- Weekly publication review preparation: Assemble the execution summary, test coverage audit, and issue disposition list to support DPQ’s Monday release recommendation.
WHY THIS ROLE, WHY NOW
Komodo’s Data Product org is evolving—formalizing quality ownership, embedding automated QA, and establishing DPQ as the independent voice for every release. You’re joining at the moment that authority is being defined and put into practice.
LOOKING BACK ON YOUR FIRST 12 MONTHS AT KOMODO HEALTH, YOU WILL HAVE…
I. DATA OUTPUT VALIDATION (PRIMARY FOCUS)
The core of this role is independently assessing whether Komodo’s data outputs are analytically sound—ensuring the data tells the right story for customers, not just that the engineering works.
- Release validation: Design and run pre/post-release comparisons across key attributes (patient counts, claim volumes, fill rates, deduplication, payer attribution, provider coverage).
- Anomaly identification: Surface and document issues missed by automated tests—valid but suspicious patterns like demographic shifts, volume changes, or rule edge cases.
- Disposition recommendations: For each issue, assess what changed, customer impact, and recommend action (approve, conditional approve, hold, or escalate).
- Coverage documentation: Track what was tested, what passed, and accepted risks for each release—creating an auditable quality trail.
II. BUG INVESTIGATION & ROOT CAUSE ANALYSIS
Data quality issues rarely surface clearly—this role requires the rigor to navigate ambiguity and the precision to communicate findings to both technical and non-technical stakeholders.
- Issue triage: Review and prioritize DPQ Jira issues, distinguishing data output problems (DPQ), pipeline failures (engineering), and cases needing joint investigation.
- Hands-on investigation: Query Snowflake to trace anomalies to source, validate against expectations, and rule out alternatives.
- Findings documentation: Produce clear reports outlining the issue, evidence, likely cause, and next steps for both technical and non-technical audiences.
- Resolution coordination: Partner with Data Engineering and Architects to drive resolution and verify fixes address the root issue.
III. WEEKLY PUBLICATION REVIEW PREPARATION
Each week, Komodo publishes updated data. DPQ owns the release recommendation at the Monday meeting, and this role assembles the inputs that inform that decision.
- Pipeline execution summary: Compile a weekly record of which data pipelines ran, their completion status, any anomalies in run time or output volume, and a comparison against expected behavior.
- Test coverage audit: For each pipeline that ran during the week, document which quality checks were expected to execute and which did, surfacing any gaps in coverage that require manual review or escalation.
- Issue consolidation: Aggregate all quality issues raised during the week — from automated alerts, manual testing, and customer reports — into a single structured view with status, severity, and recommended disposition.
- Release recommendation package: Prepare the DPQ release recommendation document in advance of the Monday meeting, enabling DPQ leadership to review and present a confident, evidence-based recommendation.
WHAT YOU BRING TO KOMODO HEALTH
This role rewards people who are energized by the gap between “it ran as designed” and “it’s actually right.” The ideal candidate is:
- Intellectually curious: Driven to investigate the unknown in complex data—never taking outputs at face value and always digging for root cause.
- Analytically rigorous: Go beyond error checks—spot missing data, unexpected trends, and subtle signals that something’s off.
- Customer-aware: Define quality by whether customers can answer real healthcare questions—not just whether tests pass.
- Precise communicator: Write clear, actionable reports for engineers and accessible summaries for non-technical partners.
- Operationally reliable: Consistently deliver a complete, on-time release package for the weekly publication meeting.
TECHNICAL SKILLS & EXPERIENCE
- Experience: 4+ years of experience in data quality, data analysis, or analytics engineering — preferably in healthcare, life sciences, or another domain with complex, multi-source data.
- SQL proficiency: Strong SQL skills for large-scale analysis—joins, window functions, aggregations, and tracing data lineage. Snowflake preferred.
- Data investigation: Proven ability to work through ambiguous issues from signal to root cause—ruling out as well as ruling in.
- Structured QA process: Experience designing or executing pre/post release testing, including defining attributes, tolerances, and escalation criteria.
- Python (preferred): Comfortable using Python for analysis, validation, and light automation—able to read and adapt existing scripts.
- Healthcare data (required): Familiarity with claims data (medical, pharmacy, enrollment) and common quality patterns and failure modes.
EXPECTATIONS OF AI USE IN THIS ROLE (REQUIRED)
Ability to leverage AI tools (Gemini, Claude, Cursor, etc.) to enhance personal productivity, streamline workflows, content and visualization creation.
WHAT THIS ROLE IS NOT
We want candidates to be clear-eyed about the scope of this role:
- This is not a data engineering role—you won’t build or maintain pipelines.
- This is not a software QA role—you won’t write unit tests, manage CI/CD, or review code.
- This is not a pure reporting role — you will be doing hands-on analytical work with real data, not summarizing dashboards others built.
- This is a data-first role where analytical judgment and quality intuition matter as much as execution.
The pay range for each job posting reflects a minimum and maximum range of annual base pay that we reasonably expect to pay for this position within the US. We carefully consider multiple business-related factors when determining compensation, including job-related skills, work experience, geographic work location, relevant training and certifications, business needs and market demands. The starting annual base pay for this role is listed below. This position may be eligible for performance-based bonuses as determined in the Company’s sole discretion and in accordance with a written agreement or plan. This role may also be eligible for equity awards. In addition, this role is eligible for benefits including, but not limited to, comprehensive health, dental, and vision insurance; flexible time off and holidays; 401(k) with company match; disability insurance and life insurance; and leaves of absence in accordance with applicable state and local laws and regulations and company policy.
COMPENSATION
- San Francisco Bay Area And New York City: $147,000—$199,000 USD
- All Other US Locations: $128,000—$173,000 USD
KOMODO'S AI STANDARD
At Komodo, we're not just witnessing the AI revolution – we're leading it. This is a pivotal moment in time, where being first to market with AI transforms industries and sets the bar. We've already established industry leadership in leveraging AI to revolutionize healthcare, and we expect every team member to contribute. AI here isn't optional; it's foundational. We expect you to integrate AI into your daily work – from summarizing documents to automating workflows and uncovering insights. This isn't just about efficiency; it's about making every moment more meaningful, building on trust in AI, and driving our collective success.
WHERE YOU’LL WORK
Komodo Health has a hybrid work model with hubs in San Francisco, New York City, and Chicago. Roles vary — some can be performed from anywhere in the country, others are scoped to a specific region, and some are based near one of our hubs. For hub-based Dragons, we're building intentional in-office rhythms alongside the flexibility that's core to how we work. Whatever your setup, expectations will always be clear before you join.
EQUAL OPPORTUNITY STATEMENT
Komodo Health provides equal employment opportunities to all applicants and employees. We prohibit discrimination and harassment of any type with regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. By submitting your application, you acknowledge that you have read and understand Komodo Health’s Privacy Notice for Employees and Contractors. This notice explains how we collect, use, and retain applicant data.
How to Get Visa Sponsorship as a Data Quality Analyst
Lead with your data toolset
Hiring managers for Data Quality Analyst roles scan resumes for SQL, Python, and ETL tools first. List the specific tools you've used on projects or coursework. Generic mentions of 'data experience' won't stand out in a competitive applicant pool.
Target STEM-designated employers early
Not all employers are set up to support STEM OPT extensions. Prioritize companies that have sponsored STEM OPT before. This matters more than company size and avoids scrambling for an extension before your initial 12 months run out.
Address OPT proactively in your cover letter
Briefly state your OPT authorization type and end date in your cover letter. Employers unfamiliar with STEM OPT often assume shorter timelines. Clarifying upfront removes a common reason hiring managers deprioritize international candidates.
Frame academic projects as real data experience
Capstone projects, thesis work, or coursework involving data validation and quality checks count as relevant experience. Describe the dataset size, tools used, and outcomes. Employers evaluate impact, not just whether the experience was paid work.
Emphasize domain knowledge alongside technical skills
Data quality work is highly context-specific. If your background touches healthcare data, financial records, or logistics, highlight it. Domain-matched candidates are significantly easier to onboard and far more likely to clear initial resume screens.
Apply to mid-sized companies with data infrastructure needs
Mid-sized companies in regulated industries often have the most urgent need for data quality work and shorter hiring cycles than large enterprises. They're also more flexible on OPT sponsorship when the candidate clearly solves a real business problem.
Data Quality Analyst jobs are hiring across the US. Find yours.
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Get Access To All JobsFrequently Asked Questions
Do Data Quality Analyst roles qualify for the STEM OPT extension?
Most Data Quality Analyst positions qualify for the 24-month STEM OPT extension, giving you up to 36 months of total work authorization. Qualifying degrees typically include computer science, information systems, statistics, and data science. Your employer must be enrolled in E-Verify to support the extension, so confirm this before accepting an offer.
How do I find Data Quality Analyst jobs that are open to OPT students?
Browse Migrate Mate to find Data Quality Analyst roles filtered specifically for OPT work authorization. Many general job boards don't let you filter by visa sponsorship willingness, which means you spend significant time applying to roles that won't move forward. Migrate Mate surfaces positions where employers are already open to OPT candidates.
What degree backgrounds do employers accept for Data Quality Analyst roles on OPT?
Employers most commonly hire from computer science, information systems, statistics, data engineering, and applied mathematics programs. Some roles in healthcare or finance also accept public health informatics or quantitative finance backgrounds. The key factor is demonstrating hands-on experience with data validation, profiling, or pipeline work regardless of exact degree title.
Can I start a Data Quality Analyst job before my STEM OPT extension is approved?
Yes, if you filed your STEM OPT extension application before your initial OPT EAD expired, you receive an automatic 180-day cap-gap that lets you continue working while USCIS processes the extension. You should not stop working during this period, but confirm with your DSO that your I-20 reflects the extension request before your EAD expiration date.
What technical skills should I prioritize to get hired as a Data Quality Analyst on OPT?
SQL is non-negotiable for almost every Data Quality Analyst role. Beyond that, employers prioritize experience with data profiling tools, ETL pipelines, and Python or R for validation scripts. Familiarity with data governance frameworks and experience documenting data lineage are increasingly valued, especially in finance, healthcare, and enterprise software environments.
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