{"schemaVersion":"jobsearcher.job.v1","id":"80a7af4aab86d4aefb7c9059","url":"https://jobsearcher.com/jobs/80a7af4aab86d4aefb7c9059","canonicalUrl":"https://jobsearcher.com/jobs/80a7af4aab86d4aefb7c9059","title":"Risk Data AI/ML Engineer","description":"Overview Shape a brighter financial future with us.\r\nEmployee Applicant Privacy Notice\r\nWho we are: Shape a brighter financial future with us. Together with our members, we're changing the way people think about and interact with personal finance. We're a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we're at the forefront. We're proud to come to work every day knowing that what we do has a direct impact on people's lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.\r\nThe role: We are seeking a Senior Data Engineer to join our Risk Data Team as a hands-on technical lead supporting Credit, Collections, and Fraud. This role blends deep production data engineering with formal technical and people leadership. You will own architectural decisions for the Risk data platform, define modeling standards, elevate engineering rigor, and build scalable data systems that power risk decisioning across the organization. This role exists to ensure that Risk data pipelines are reliable, well-modeled, observable, and built with long-term maintainability in mind. You will contribute directly to production data pipelines while setting standards for data modeling, dbt architecture, code quality, and observability. This is not an architect-only or strategy-only role — it requires hands-on execution and demonstrated team leadership ownership.\r\nWhat you'll do\r\nTechnical Leadership\r\nServe as technical lead for the Risk Data Engineering team.\r\nOwn architectural decisions and data modeling strategy across the Risk domain.\r\nDefine naming conventions, modeling standards, and layered dbt architecture (staging ? intermediate ? marts).\r\nLead architecture discussions and technical planning sessions.\r\nConduct code reviews focused on maintainability, readability, and long-term scalability.\r\nTranslate business priorities into well-scoped, production-ready technical deliverables.\r\nProduction Data Engineering\r\nDesign and build production-grade Snowflake data models.\r\nDevelop scalable dbt projects, including reusable macros and testing frameworks.\r\nManage Apache Airflow DAGs, including idempotency, retry logic, and failure handling.\r\nImplement CI/CD best practices for dbt and data pipelines.\r\nDrive automation initiatives to reduce manual operational overhead.\r\nData Modeling\r\nDesign dimensional and relational models aligned to business definitions.\r\nApply modeling best practices including grain declaration, SCD strategies, and surrogate key management.\r\nBalance normalization and performance trade-offs.\r\nEvolve models safely as business requirements change.\r\nEnsure all models are clearly documented with lineage and business logic.\r\nData Quality & Observability\r\nOwn the dbt testing framework (schema tests, custom tests, generic tests).\r\nDefine and enforce freshness checks, SLA standards, and row-count validations.\r\nImplement monitoring and observability using DataDog.\r\nProactively identify and reduce reliability incidents.\r\nEstablish measurable data quality SLAs in partnership with stakeholders.\r\nPeople Leadership\r\nParticipate in hiring, onboarding, and team building.\r\nRun regular 1:1s and provide structured performance feedback.\r\nDevelop engineers toward ownership and technical growth.\r\nAddress underperformance early and constructively.\r\nFoster a culture of accountability, documentation, and engineering excellence.\r\nCollaboration & Stakeholder Engagement\r\nPartner with Risk Data Product Managers, Data Science, ML, and business stakeholders.\r\nCommunicate modeling decisions, trade-offs, and pipeline health clearly.\r\nInfluence cross-functional technical direction across Risk and platform teams.\r\nOperational Excellence\r\nMaintain scalable, secure data systems aligned with enterprise governance standards.\r\nImprove documentation practices including runbooks and architecture decision records.\r\nContribute to workforce planning and technical roadmap discussions.\r\nThis role requires collaboration during core business hours. Remote candidates must be able to work cross-functionally with distributed teams.\r\nWhat you'll need\r\nBachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field (or equivalent work experience).\r\n8+ years of hands-on data engineering experience.\r\n2+ years of experience serving as a tech lead or leading engineers formally.\r\nDeep expertise in dimensional and relational data modeling, including SCD strategies and grain design.\r\nAdvanced dbt experience, including layered architecture, macros, advanced testing, and semantic layer concepts.\r\nStrong hands-on Snowflake experience, including modeling and performance optimization.\r\nProduction-level experience managing Apache Airflow DAGs.\r\nAdvanced SQL skills, including query optimization and performance tuning.\r\nStrong Python skills for data pipeline development and automation.\r\nDemonstrated ownership of a data quality and monitoring framework.\r\nExperience working in regulated or high-accuracy environments.\r\nExperience participating in hiring, onboarding, and performance management.\r\nStrong communication skills and ability to influence cross-functional stakeholders.\r\nNice to have\r\nExperience with Snowflake advanced capabilities (Snowpark, Cortex AI, ML functions).\r\nFamiliarity with LLM tooling, RAG systems, or AI-assisted data workflows.\r\nFinancial services experience (Credit, Fraud, Collections).\r\nAWS experience (S3, Glue, Lambda) and infrastructure-as-code familiarity.\r\nExperience implementing data governance frameworks at scale.\r\nCompensation And Benefits\r\nThe base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate's experience, skills, and location. To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page. SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law. The Company hires the best qualified candidate for the job, without regard to protected characteristics. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. New York applicants: Notice of Employee Rights. SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com. Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time. Internal Employees. If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.\r\nAbout the company\r\nSoFi\r\nBe vigilant about potential scams, phishing attempts, or fraudulent activities, and seek credible sources or reviews to assess the trustworthiness of the company. Remember, your personal and financial security is paramount, and taking preventive measures is crucial to safeguarding your information from potential risks and unauthorized use. SupportFinity is not responsible for any consequences that may arise from disclosing such information to unauthorized or fraudulent entities.\r\nJ-18808-Ljbffr","company":"SupportFinity","rawCompany":"supportfinity","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-09T15:34:59.538Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Risk Data AI/ML Engineer","description":"Overview Shape a brighter financial future with us.\r\nEmployee Applicant Privacy Notice\r\nWho we are: Shape a brighter financial future with us. Together with our members, we're changing the way people think about and interact with personal finance. We're a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we're at the forefront. We're proud to come to work every day knowing that what we do has a direct impact on people's lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.\r\nThe role: We are seeking a Senior Data Engineer to join our Risk Data Team as a hands-on technical lead supporting Credit, Collections, and Fraud. This role blends deep production data engineering with formal technical and people leadership. You will own architectural decisions for the Risk data platform, define modeling standards, elevate engineering rigor, and build scalable data systems that power risk decisioning across the organization. This role exists to ensure that Risk data pipelines are reliable, well-modeled, observable, and built with long-term maintainability in mind. You will contribute directly to production data pipelines while setting standards for data modeling, dbt architecture, code quality, and observability. This is not an architect-only or strategy-only role — it requires hands-on execution and demonstrated team leadership ownership.\r\nWhat you'll do\r\nTechnical Leadership\r\nServe as technical lead for the Risk Data Engineering team.\r\nOwn architectural decisions and data modeling strategy across the Risk domain.\r\nDefine naming conventions, modeling standards, and layered dbt architecture (staging ? intermediate ? marts).\r\nLead architecture discussions and technical planning sessions.\r\nConduct code reviews focused on maintainability, readability, and long-term scalability.\r\nTranslate business priorities into well-scoped, production-ready technical deliverables.\r\nProduction Data Engineering\r\nDesign and build production-grade Snowflake data models.\r\nDevelop scalable dbt projects, including reusable macros and testing frameworks.\r\nManage Apache Airflow DAGs, including idempotency, retry logic, and failure handling.\r\nImplement CI/CD best practices for dbt and data pipelines.\r\nDrive automation initiatives to reduce manual operational overhead.\r\nData Modeling\r\nDesign dimensional and relational models aligned to business definitions.\r\nApply modeling best practices including grain declaration, SCD strategies, and surrogate key management.\r\nBalance normalization and performance trade-offs.\r\nEvolve models safely as business requirements change.\r\nEnsure all models are clearly documented with lineage and business logic.\r\nData Quality & Observability\r\nOwn the dbt testing framework (schema tests, custom tests, generic tests).\r\nDefine and enforce freshness checks, SLA standards, and row-count validations.\r\nImplement monitoring and observability using DataDog.\r\nProactively identify and reduce reliability incidents.\r\nEstablish measurable data quality SLAs in partnership with stakeholders.\r\nPeople Leadership\r\nParticipate in hiring, onboarding, and team building.\r\nRun regular 1:1s and provide structured performance feedback.\r\nDevelop engineers toward ownership and technical growth.\r\nAddress underperformance early and constructively.\r\nFoster a culture of accountability, documentation, and engineering excellence.\r\nCollaboration & Stakeholder Engagement\r\nPartner with Risk Data Product Managers, Data Science, ML, and business stakeholders.\r\nCommunicate modeling decisions, trade-offs, and pipeline health clearly.\r\nInfluence cross-functional technical direction across Risk and platform teams.\r\nOperational Excellence\r\nMaintain scalable, secure data systems aligned with enterprise governance standards.\r\nImprove documentation practices including runbooks and architecture decision records.\r\nContribute to workforce planning and technical roadmap discussions.\r\nThis role requires collaboration during core business hours. Remote candidates must be able to work cross-functionally with distributed teams.\r\nWhat you'll need\r\nBachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field (or equivalent work experience).\r\n8+ years of hands-on data engineering experience.\r\n2+ years of experience serving as a tech lead or leading engineers formally.\r\nDeep expertise in dimensional and relational data modeling, including SCD strategies and grain design.\r\nAdvanced dbt experience, including layered architecture, macros, advanced testing, and semantic layer concepts.\r\nStrong hands-on Snowflake experience, including modeling and performance optimization.\r\nProduction-level experience managing Apache Airflow DAGs.\r\nAdvanced SQL skills, including query optimization and performance tuning.\r\nStrong Python skills for data pipeline development and automation.\r\nDemonstrated ownership of a data quality and monitoring framework.\r\nExperience working in regulated or high-accuracy environments.\r\nExperience participating in hiring, onboarding, and performance management.\r\nStrong communication skills and ability to influence cross-functional stakeholders.\r\nNice to have\r\nExperience with Snowflake advanced capabilities (Snowpark, Cortex AI, ML functions).\r\nFamiliarity with LLM tooling, RAG systems, or AI-assisted data workflows.\r\nFinancial services experience (Credit, Fraud, Collections).\r\nAWS experience (S3, Glue, Lambda) and infrastructure-as-code familiarity.\r\nExperience implementing data governance frameworks at scale.\r\nCompensation And Benefits\r\nThe base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate's experience, skills, and location. To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page. SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law. The Company hires the best qualified candidate for the job, without regard to protected characteristics. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. New York applicants: Notice of Employee Rights. SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com. Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time. Internal Employees. If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.\r\nAbout the company\r\nSoFi\r\nBe vigilant about potential scams, phishing attempts, or fraudulent activities, and seek credible sources or reviews to assess the trustworthiness of the company. Remember, your personal and financial security is paramount, and taking preventive measures is crucial to safeguarding your information from potential risks and unauthorized use. SupportFinity is not responsible for any consequences that may arise from disclosing such information to unauthorized or fraudulent entities.\r\nJ-18808-Ljbffr","datePosted":"2026-04-09T15:34:59.538Z","dateModified":"2026-04-09T15:34:59.538Z","hiringOrganization":{"@type":"Organization","name":"SupportFinity","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"80a7af4aab86d4aefb7c9059"},"url":"https://jobsearcher.com/jobs/80a7af4aab86d4aefb7c9059"}}