Senior SDET – Data [Remote-US]

We need to output HTML fragment with sections: About Us? Actually the posting includes "About Us" and "Our Team". The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It says we should not repeat the job title as a heading. It says include a section ONLY when posting has real, specific content for it. We need to decide which sections to include based on content. We have content: About Us (mission, description). Our Team (description). The Role (description). Your Day-to-Day (list of duties). About You (requirements/qualifications). Bonus Points (additional qualifications). Salary (pay). Additional Details: Benefits, Work from Home Equipment, Paid Time Off, Personal and Professional Development, Location, Hours. We need to map to headings: About the role? Actually we have "The Role" which is description of role. Could map to "About the role". Responsibilities: we have "Your Day-to-Day" which is duties. Requirements/Qualifications: "About You" plus maybe "Bonus Points". Skills: maybe not separate; we could combine. Benefits: we have benefits details. Pay: we have salary range. Schedule: we have Hours (core meeting hours). Also Location maybe not a standard heading; but we could include as a paragraph under maybe "Location" but not a heading not in list; but instruction says use those headings for sections; we could still add other headings? It says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but suggests those as examples. Probably we should stick to those headings where applicable. Location could be included under a paragraph maybe not as heading. Hours could be under Schedule. We need to remove boilerplate: security warning, EEO, etc. Also remove duplicated lines, application instructions. We need to preserve all substantive information. Let's extract substantive content: About Us: "Quanata is on a mission to help ensure a better world through context-based insurance solutions. We are an exceptional, customer centered team with a passion for creating innovative technologies, digital products, and brands. We blend some of the best Silicon Valley talent and cutting-edge thinking with the long-term backing of leading insurer, State Farm.Learn more about us and our work at quanata.com" Our Team: "Quanata, LLC is an insurance technology innovation company that engineers advanced risk prediction and prevention solutions, develops risk-focused acquisition capabilities, and builds/supports a full-stack, flexible, digital & increasingly AI-native insurance platform. This helps our primary clients, State Farm and HiRoad Assurance Company, adapt to evolving market needs. Quanata, LLC is wholly owned and funded by State Farm. As a company that prioritizes an inclusive and positive culture, we believe the core of our success is in hiring talented people — across disciplines — who want to help us make a quantifiable impact." The Role: "Join our Data Platform QA team as a hands-on engineer focused on ensuring the quality, reliability, and performance of complex data platforms and machine learning workflows. In this individual contributor role, you’ll design scalable approaches to automated testing and quality assurance across data and ML systems. You’ll partner closely with Data Engineers, ML Engineers, Data Scientists, and DevOps teams to identify risks early, strengthen quality throughout the development lifecycle, and ensure data and model outputs can be trusted. You’ll also help shape testing practices across teams, troubleshoot complex production issues, and mentor other quality engineers while remaining actively involved in hands-on engineering." Your Day-to-Day (list): - Design and implement scalable automated testing frameworks for complex data pipelines, processing workflows, and machine learning models. - Develop comprehensive testing strategies that address data quality, schema integrity, data drift, model accuracy, bias, and regression. - Validate large-scale distributed data systems for accuracy, reliability, performance, and resilience. - Partner across engineering, data science, and operations teams to embed quality throughout the software development and delivery lifecycle. - Conduct exploratory testing to identify edge cases and risks in new features, data outputs, and machine learning workflows. - Build continuous validation into automated development and deployment processes. - Investigate production data and model failures, identify root causes, and help drive effective resolutions. - Mentor junior SDETs and contribute to broader testing strategies, standards, and quality engineering best practices. About You (requirements): - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or an equivalent combination of education and relevant experience. - Typically 6–8 years of software testing experience, including at least 3 years focused on data platform or machine learning testing. - Strong programming skills with experience building and maintaining automated testing frameworks. - Hands-on experience testing complex data platforms, pipelines, and distributed data processing systems. - Strong understanding of data validation approaches, including data quality, integrity, consistency, and schema validation. - Understanding of machine learning concepts, workflows, and model evaluation methods. - Experience developing automated validation approaches for data and machine learning systems. - Deep understanding of continuous integration, continuous delivery, and infrastructure automation practices. - Ability to troubleshoot complex data and model failures and contribute effectively to root cause analysis. - Ability to collaborate effectively across engineering, data science, and operations teams. - Strong analytical and problem-solving skills, with the ability to identify risks and edge cases in complex systems. - Ability to mentor other quality engineers and contribute to shared testing standards and best practices. Bonus Points: - Experience evaluating machine learning models for interpretability, fairness, and bias. - Familiarity with machine learning operations and model lifecycle practices. - Experience monitoring the health and performance of production data pipelines and machine learning systems. - Knowledge of data governance and compliance practices. Salary: $198,000 to $281,000* (note about final salary determined based on skills etc.) Additional Details: Benefits: We provide a wide variety of health, wellness and other benefits. These include medical, dental, vision, life insurance and supplemental income plans for you and your dependents, a Headspace app subscription, monthly wellness allowance and a 401(k) Plan with a company match. Work from Home Equipment: Given our virtual environment— in order to set you up for success at home, a one-time payment of $2K will be provided to cover the purchase of in-home office equipment and furniture at your discretion. Also, our teams work with MacBook Pros, which we will deliver to you fully provisioned prior to your first day. Paid Time Off: All employees accrue four weeks of PTO in their first year of employment. New parents receive twelve weeks of fully paid parental leave which may be taken within one year after the birth and/or adoption of a child. The twelve weeks is applicable to both birthing and non-birthing parent. Personal and Professional Development: We’re committed to investing in and helping our people grow personally and professionally. All employees receive up to $5000 each year for professional learning, continuing education and career development. All team members also receive LinkedIn Learning subscriptions and access to multiple different coaching opportunities through BetterUp. Location: We are a remote-first company for most positions so you may work from anywhere you like in the U.S, excluding U.S. territories. For most positions, occasional travel may be requested or encouraged but is not required. Some positions might require travel per the job description provided to the employee. Employees based in the San Francisco Bay Area or in Providence, Rhode Island may commute to one of our local offices as desired. Hours: We maintain core meeting hours from 9AM - 2PM Pacific time for collaborating with team members across all time zones. We need to output HTML fragment with

headings for sections we include. Potential sections: - About the role (could combine About Us + Our Team + The Role? Actually "About the role" likely refers to role description. But we also have About Us and Our Team which are company info. Could we put them under "About the role"? Not ideal. Maybe we create separate sections: "About Us" and "Our Team" but those aren't in the list. However instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings but suggests those as examples. Safer to stick to those headings. We could incorporate About Us and Our Team into a brief paragraph under "About the role" or maybe under a generic "About the company" but not listed. Could we omit them? The instruction says preserve ALL substantive information. So we need to include that info somewhere. We could put it under a heading like "About the company" but that's not in the list; but maybe it's okay as long as we use

. The instruction didn't say we cannot use other headings, just gave examples. So we can use

for any section heading we need, but we should follow the pattern. Probably acceptable to use headings like "About Us", "Our Team", "About the role", "Responsibilities", "Requirements", "Qualifications", "Benefits", "Pay", "Schedule". We'll include those. We must not repeat the job title as a heading. The job title is not explicitly given but likely "Data Platform QA Engineer". Not present as heading. We must not put Job ID/requisition numbers as headings. We must not include sections with filler like "commensurate with experience". We have salary range, so Pay section is valid. We must not create Benefits section unless posting actually states them; we have benefits details, so yes. Schedule: we have core meeting hours, so yes. We need to output only HTML fragment, no /, no markdown. We'll structure:

About Us

...

Our Team

...

About the role

...

Responsibilities

• ...
...

Requirements

• ...
...

Maybe also Qualifications separate? Could combine Requirements and Qualifications. But we have Bonus Points; could be under "Preferred Qualifications" or "Additional Qualifications". We'll make a section "Preferred Qualifications" for Bonus Points.

Preferred Qualifications

...

Benefits

...

(list each benefit)

Pay

Salary: $198,000 to $281,000 (final salary determined based on skills, experience, and internal salary structure).

Schedule

Core meeting hours: 9AM - 2PM Pacific time.

We also have Location and Work from Home Equipment, Paid Time Off, Personal and Professional Development. Those could be under Benefits? Or we could add sections like "Work Arrangements", "Time Off", "Professional Development". But not in list. However we need to preserve all substantive info. Could incorporate them under Benefits as sub-items? Might be okay to add extra sections like "Work Arrangements", "Time Off", "Professional Development". The instruction didn't forbid extra headings, just gave examples. So we can add. We'll add:

Work Arrangements

Remote-first (U.S. excluding territories); occasional travel optional; MacBook Pro provided; $2,000 home office stipend.

Time Off

Four weeks PTO first year; twelve weeks fully paid parental leave for new parents.

Professional Development

Up to $5,000 per year for learning; LinkedIn Learning subscription; BetterUp coaching access.

We also have Location detail: remote-first, can work anywhere in US excluding territories; occasional travel; SF Bay Area or Providence RI offices optional. Could include in Work Arrangements. Now ensure we removed boilerplate: security warning