{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/dpa"},"x-facet":{"type":"skill","slug":"dpa","display":"Dpa","count":2},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. 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This role owns the strategy and execution of Cresta’s most complex, non-standard enterprise deals, acting as the quarterback from quote to close.</p>\n<p>Reporting to Lauren Jungers, Sr. Director of Commercial Strategy &amp; Operations, this role works closely with Sales leadership, Finance, Legal, Security, and RevOps to structure deals that accelerate revenue while protecting Cresta’s long-term business interests.</p>\n<p>This is an individual contributor role (no direct people management).</p>\n<p><strong>Deal Strategy &amp; Execution</strong> Partner with US and international Sales teams to architect and execute complex, high-value enterprise transactions Build creative deal structures, pricing models, and commercial terms that balance speed, risk, margin, and customer experience Own approval workflows and act as escalation owner from first quote through final signature Translate deal data, discount trends, and outcomes into actionable insights Lead win/loss and deal retrospectives to institutionalize learnings and refine deal strategy Ensure deal structures align with revenue recognition principles and company financial goals</p>\n<p><strong>Process &amp; Systems Modernization</strong> Drive end-to-end improvements across the quote-to-cash lifecycle Define business requirements and partner with systems teams on Subskribe, CLM, and Salesforce enhancements Lead UAT and rollout of new deal processes and tooling Represent Deal Strategy &amp; Operations as a key stakeholder on high-priority cross-functional initiatives Standardize and document workflows to enable speed and scale</p>\n<p><strong>Enablement &amp; Advisory</strong> Provide ongoing training to Sales on pricing, deal construction, and quoting with a strong focus on the why behind the strategy Contribute to and maintain a self-service Deal Strategy Playbook</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_17e16445-23f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Cresta","sameAs":"https://www.cresta.ai/","logo":"https://logos.yubhub.co/cresta.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/cresta/jobs/5154290008","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$140,000–$175,000 base + Offers Equity","x-skills-required":["Deal Desk","Deal Strategy","Commercial Operations","Revenue Operations","Legal Ops","Sales Operations","CPQ tools","Subskribe","Salesforce","Enterprise SaaS contracting concepts","Order forms","MSA","DPA","SOW"],"x-skills-preferred":["Salesforce Admin experience or certification","Legal","Contract management","Pricing strategy background","Prior sales or customer-facing experience"],"datePosted":"2026-04-18T15:57:32.437Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"United States (Remote)"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Sales","industry":"Technology","skills":"Deal Desk, Deal Strategy, Commercial Operations, Revenue Operations, Legal Ops, Sales Operations, CPQ tools, Subskribe, Salesforce, Enterprise SaaS contracting concepts, Order forms, MSA, DPA, SOW, Salesforce Admin experience or certification, Legal, Contract management, Pricing strategy background, Prior sales or customer-facing experience","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":140000,"maxValue":175000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_e503559e-cf7"},"title":"Senior Machine Learning Engineer","description":"<p><strong>Job Title: Senior Machine Learning Engineer</strong></p>\n<p><strong>Job Description:</strong></p>\n<p>Before 1965, it was extremely difficult and time-consuming to analyze complicated signals, like radio or images. You could solve it, but you had to throw a ton of compute at it. That all changed with the invention of the Fast Fourier transform, which could efficiently break that signal down into the frequencies that are a part of it.</p>\n<p>The Risk Onboarding team is working on efficiently reviewing customers’ applications without compromising on quality. We are the front line of defense for preventing money laundering and financial crimes, building systems to verify that someone is who they say they are and that we are allowed to do business with them.</p>\n<p><strong>About Us:</strong></p>\n<p>At Mercury, we craft an exceptional banking experience for startups. Our team is focused on ensuring our products create a safe environment that meets the needs of our customers, administrators, and regulators.</p>\n<p><strong>Job Responsibilities:</strong></p>\n<p>As part of this role, you will:</p>\n<ul>\n<li>Partner with data science &amp; engineering teams to design and deploy ML &amp; Gen AI microservices, primarily focusing on automating reviews</li>\n<li>Work with a full-stack engineering team to embed these services into the overall review experience, including human in the loop, escalations, and feeding human decisions back into the service</li>\n<li>Implement testing, observability, alerting, and disaster recovery for all services</li>\n<li>Implement tracing, performance, and regression testing</li>\n<li>Feel a strong sense of product ownership and actively seek responsibility – we often self-organize on small/medium projects, and we want someone who’s excited to help shape and build Mercury’s future</li>\n</ul>\n<p><strong>Ideal Candidate:</strong></p>\n<p>The ideal candidate for the role has:</p>\n<ul>\n<li>7+ years of experience in roles like machine learning engineering, data engineering, backend software engineering, and/or devops</li>\n<li>Expertise with:</li>\n</ul>\n<ul>\n<li>A full modern data stack: Snowflake, dbt, Fivetran, Airbyte, Dagster, Airflow</li>\n<li>SQL, dbt, Python</li>\n<li>OLAP / OLTP data modelling and architecture</li>\n<li>Key-value stores: Redis, dynamoDB, or equivalent</li>\n<li>Streaming / real-time data pipelines: Kinesis, Kafka, Redpanda</li>\n<li>API frameworks: FastAPI, Flask, etc.</li>\n<li>Production ML Service experience</li>\n<li>Working across full-stack development environment, with experience transferable to Haskell, React, and TypeScript</li>\n</ul>\n<p><strong>Total Rewards Package:</strong></p>\n<p>The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.</p>\n<p><strong>Salary Range:</strong></p>\n<p>Our target new hire base salary ranges for this role are the following:</p>\n<ul>\n<li>US employees (any location): $200,700 - $250,900</li>\n<li>Canadian employees (any location): CAD 189,700 - 237,100</li>\n</ul>\n<p><strong>Diversity &amp; Belonging:</strong></p>\n<p>Mercury values diversity &amp; belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_e503559e-cf7","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mercury","sameAs":"https://www.mercury.com/","logo":"https://logos.yubhub.co/mercury.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/mercury/jobs/5639559004","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$200,700 - $250,900 (US) | CAD 189,700 - 237,100 (Canada)","x-skills-required":["Snowflake","dbt","Fivetran","Airbyte","Dagster","Airflow","SQL","Python","OLAP / OLTP data modelling and architecture","Redis","dynamoDB","Kinesis","Kafka","Redpanda","FastAPI","Flask","Production ML Service experience","Haskell","React","TypeScript"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:45:16.566Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Finance","skills":"Snowflake, dbt, Fivetran, Airbyte, Dagster, Airflow, SQL, Python, OLAP / OLTP data modelling and architecture, Redis, dynamoDB, Kinesis, Kafka, Redpanda, FastAPI, Flask, Production ML Service experience, Haskell, React, TypeScript","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":189700,"maxValue":250900,"unitText":"YEAR"}}}]}