{"schemaVersion":"jobsearcher.job.v1","id":"e88fa5fe810d82bda87d692a","url":"https://jobsearcher.com/jobs/e88fa5fe810d82bda87d692a","canonicalUrl":"https://jobsearcher.com/jobs/e88fa5fe810d82bda87d692a","title":"Senior Data Engineer","description":"Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high-ROI, workforce-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.\nThe Impact You Will Make\nAs a Senior Data Engineer on the Enterprise Data Platform team, you will build and operate the governed data products that power analytics, reporting, and AI/ML for the 100+ universities and colleges Risepoint supports. Your pipelines and dimensional models turn raw operational and CRM data into trusted insight that drives enrollment, retention, and student success. As a technical lead on the team, you will also translate requirements for and coordinate delivery with offshore engineering partners, review their work for quality, and help set the standards that keep our data trustworthy as the platform scales.\nHow You Will Bring Our Mission to Life\nWorking hands-on in Databricks and dbt, you will contribute to design scalable pipelines on a Delta Lakehouse, model data using Kimball dimensional methodology, and operationalize machine learning with MLflow and MLOps practices. You will partner closely with data architects, analysts, and business stakeholders to keep the data behind Risepoint’s decisions accurate, timely, and well-governed.\nWhat You Will Do\nDesign, build, and own scalable data pipelines and dimensional models on Databricks (PySpark, SQL, medallion architecture) — delivering trusted, on-time data products and meeting SLAs within your assigned scope.\nIngest data from operational and SaaS sources such as Salesforce into the lakehouse, favoring managed connectors like Lakeflow Connect where appropriate.\nBuild and maintain Kimball-style dimensional models — facts, conformed dimensions, and slowly changing dimensions — as the analytics layer of record.\nDevelop, test, and document transformations in dbt (models, sources, snapshots, tests, exposures) with strong CI discipline.\nManage data assets in Unity Catalog, including catalogs, schemas, permissions, and lineage.\nOptimize performance and cost through cluster and warehouse sizing, Spark tuning, partitioning, and tagging for cost attribution.\nOperationalize machine learning workflows using MLflow for experiment tracking, model registry, and deployment, applying MLOps best practices.\nHelp coordinating day-to-day work with offshore vendor engineering resources — setting priorities, sequencing deliverables, and keeping their work aligned to sprint commitments and the platform roadmap.\nTranslate business and technical requirements into clear specifications, acceptance criteria, and design guidance that offshore teams can execute with minimal ambiguity.\nQuality-check offshore deliverables through code review, testing, and validation against data standards, performance targets, and definition-of-done before changes are promoted to production.\nCollaborates with data architects, analysts, and business stakeholders to keep data accurate and well-governed, building alignment within the team and with immediate cross-functional partners on delivery.\nUphold engineering standards, code review practices, and documentation conventions across both onshore and offshore contributors.\nSupport the team's growth by training and coaching engineers on tools, standards, and best practices as the platform scales.\nWhat Success Looks Like\nReliable, well-modeled data products that stakeholders trust and use without rework or manual reconciliation.\nPipelines that run efficiently and cost-effectively, with issues caught proactively through monitoring rather than reported by downstream users.\nMachine learning models moved from experimentation into governed production with reproducible, monitored MLOps workflows.\nOffshore and vendor deliverables consistently meet quality and standards on first review, with minimal rework.\nRecognized as a technical lead others rely on, able to represent the team, and unblock engineers.\nHow Impact Will be Measured\nData quality, pipeline reliability, and freshness SLAs met across owned datasets.\nReduction in data incidents and in time-to-resolution for pipeline and reconciliation issues.\nOn-time delivery of dimensional models and data products that unblock analytics and AI initiatives.\nWhat You’ll Bring to the Team\nExperience That Matters Most\n7+ years in data engineering on big data and cloud platforms, including 3+ years hands-on with Databricks (Spark/PySpark, Delta Lake, jobs).\nProven delivery of Kimball / dimensional data models in a modern warehouse or lakehouse, with strong SQL and Python (PySpark).\nProduction experience with dbt (models, tests, snapshots) and with Unity Catalog for governance, access control, and lineage.\nWorking knowledge of the ML lifecycle and MLOps, including MLflow for experiment tracking, model registry, and deployment.\nExperience translating business and technical requirements into clear specifications and coordinating or overseeing offshore and vendor engineering resources, including reviewing their deliverables for quality.\nStrong communication and stakeholder skills, with a track record of mentoring engineers and setting technical standards.\nExperience That’s Great to Have\nReal-time / streaming experience (Structured Streaming, Kafka, or Azure Event Hubs) and familiarity with the Salesforce data model.\nCost governance across multi-workspace Databricks environments — cluster policies, tagging, and system.billing.usage analysis.\nBI / visualization exposure (Power BI, Tableau, or Databricks dashboards/Genie) and containerization (Docker) for reproducible workflows.\nPrior technical-lead, team-lead, or technical-management exposure.\nExperience managing vendor or partner relationships, or distributed and offshore delivery models.\nRisepoint is an equal-opportunity employer and supports a diverse and inclusive workforce.","company":"Risepoint","rawCompany":"risepoint","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-08-07T10:39:52.110Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Data Engineer","description":"Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high-ROI, workforce-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.\nThe Impact You Will Make\nAs a Senior Data Engineer on the Enterprise Data Platform team, you will build and operate the governed data products that power analytics, reporting, and AI/ML for the 100+ universities and colleges Risepoint supports. Your pipelines and dimensional models turn raw operational and CRM data into trusted insight that drives enrollment, retention, and student success. As a technical lead on the team, you will also translate requirements for and coordinate delivery with offshore engineering partners, review their work for quality, and help set the standards that keep our data trustworthy as the platform scales.\nHow You Will Bring Our Mission to Life\nWorking hands-on in Databricks and dbt, you will contribute to design scalable pipelines on a Delta Lakehouse, model data using Kimball dimensional methodology, and operationalize machine learning with MLflow and MLOps practices. You will partner closely with data architects, analysts, and business stakeholders to keep the data behind Risepoint’s decisions accurate, timely, and well-governed.\nWhat You Will Do\nDesign, build, and own scalable data pipelines and dimensional models on Databricks (PySpark, SQL, medallion architecture) — delivering trusted, on-time data products and meeting SLAs within your assigned scope.\nIngest data from operational and SaaS sources such as Salesforce into the lakehouse, favoring managed connectors like Lakeflow Connect where appropriate.\nBuild and maintain Kimball-style dimensional models — facts, conformed dimensions, and slowly changing dimensions — as the analytics layer of record.\nDevelop, test, and document transformations in dbt (models, sources, snapshots, tests, exposures) with strong CI discipline.\nManage data assets in Unity Catalog, including catalogs, schemas, permissions, and lineage.\nOptimize performance and cost through cluster and warehouse sizing, Spark tuning, partitioning, and tagging for cost attribution.\nOperationalize machine learning workflows using MLflow for experiment tracking, model registry, and deployment, applying MLOps best practices.\nHelp coordinating day-to-day work with offshore vendor engineering resources — setting priorities, sequencing deliverables, and keeping their work aligned to sprint commitments and the platform roadmap.\nTranslate business and technical requirements into clear specifications, acceptance criteria, and design guidance that offshore teams can execute with minimal ambiguity.\nQuality-check offshore deliverables through code review, testing, and validation against data standards, performance targets, and definition-of-done before changes are promoted to production.\nCollaborates with data architects, analysts, and business stakeholders to keep data accurate and well-governed, building alignment within the team and with immediate cross-functional partners on delivery.\nUphold engineering standards, code review practices, and documentation conventions across both onshore and offshore contributors.\nSupport the team's growth by training and coaching engineers on tools, standards, and best practices as the platform scales.\nWhat Success Looks Like\nReliable, well-modeled data products that stakeholders trust and use without rework or manual reconciliation.\nPipelines that run efficiently and cost-effectively, with issues caught proactively through monitoring rather than reported by downstream users.\nMachine learning models moved from experimentation into governed production with reproducible, monitored MLOps workflows.\nOffshore and vendor deliverables consistently meet quality and standards on first review, with minimal rework.\nRecognized as a technical lead others rely on, able to represent the team, and unblock engineers.\nHow Impact Will be Measured\nData quality, pipeline reliability, and freshness SLAs met across owned datasets.\nReduction in data incidents and in time-to-resolution for pipeline and reconciliation issues.\nOn-time delivery of dimensional models and data products that unblock analytics and AI initiatives.\nWhat You’ll Bring to the Team\nExperience That Matters Most\n7+ years in data engineering on big data and cloud platforms, including 3+ years hands-on with Databricks (Spark/PySpark, Delta Lake, jobs).\nProven delivery of Kimball / dimensional data models in a modern warehouse or lakehouse, with strong SQL and Python (PySpark).\nProduction experience with dbt (models, tests, snapshots) and with Unity Catalog for governance, access control, and lineage.\nWorking knowledge of the ML lifecycle and MLOps, including MLflow for experiment tracking, model registry, and deployment.\nExperience translating business and technical requirements into clear specifications and coordinating or overseeing offshore and vendor engineering resources, including reviewing their deliverables for quality.\nStrong communication and stakeholder skills, with a track record of mentoring engineers and setting technical standards.\nExperience That’s Great to Have\nReal-time / streaming experience (Structured Streaming, Kafka, or Azure Event Hubs) and familiarity with the Salesforce data model.\nCost governance across multi-workspace Databricks environments — cluster policies, tagging, and system.billing.usage analysis.\nBI / visualization exposure (Power BI, Tableau, or Databricks dashboards/Genie) and containerization (Docker) for reproducible workflows.\nPrior technical-lead, team-lead, or technical-management exposure.\nExperience managing vendor or partner relationships, or distributed and offshore delivery models.\nRisepoint is an equal-opportunity employer and supports a diverse and inclusive workforce.","datePosted":"2026-08-07T10:39:52.110Z","dateModified":"2026-08-07T10:39:52.110Z","hiringOrganization":{"@type":"Organization","name":"Risepoint","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"e88fa5fe810d82bda87d692a"},"url":"https://jobsearcher.com/jobs/e88fa5fe810d82bda87d692a"}}