高级数据工程师
Senior Data Engineer
Risepoint 是一家教育科技公司，为超过 100 所大学和学院提供世界级的支持和可信赖的专业知识。我们主要与地区性大学合作，帮助他们开发和拓展高投资回报率、以就业为导向的在线学位项目，涵盖护理、教学、商业和公共服务等关键领域。Risepoint 致力于提高可负担教育的可及性，让更多学生，特别是在职成年人，提升职业发展并满足雇主和社区的需求。
你将带来的影响
作为企业数据平台团队的高级数据工程师，你将构建和运营支持分析、报告和 AI/ML 的受控数据产品，这些产品服务于 Risepoint 支持的 100 多所大学和学院。你的数据流水线和维度模型将原始运营和 CRM 数据转化为可信的洞察，推动招生、留存和学生成功。作为团队的技术负责人，你还将翻译需求并与海外工程合作伙伴协调交付，审查他们的工作质量，并协助制定标准，确保平台扩展时数据的可信度。
你如何实现我们的使命
你将亲自在 Databricks 和 dbt 中工作，参与设计可扩展的数据流水线，使用 Delta Lakehouse 构建数据模型，采用 Kimball 维度方法论进行数据建模，并通过 MLflow 和 MLOps 实践实现机器学习的生产化。你将与数据架构师、分析师和业务利益相关者紧密合作，确保 Risepoint 决策背后的数据准确、及时且得到良好治理。
你将负责的工作
· 在 Databricks（PySpark、SQL、银币架构）上设计、构建并负责可扩展的数据流水线和维度模型——交付可信、按时的数据产品，并在你负责的范围内满足 SLA 要求。
· 从运营和 SaaS 来源（如 Salesforce）中提取数据到湖仓，适当使用 Lakeflow Connect 等托管连接器。
· 构建和维护 Kimball 风格的维度模型——事实表、一致维度和缓慢变化维度——作为记录层的分析层。
· 使用 dbt（模型、源、快照、测试、暴露）开发、测试和文档化转换过程，并具备强大的 CI 纪律。
· 管理 Unity Catalog 中的数据资产，包括目录、模式、权限和血缘关系。
· 通过集群和仓库大小调整、Spark 调优、分区和成本归因标签优化性能和成本。
查看英文原文
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.
The Impact You Will Make
As 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.
How You Will Bring Our Mission to Life
Working 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.
What You Will Do
· Design, 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.
· Ingest data from operational and SaaS sources such as Salesforce into the lakehouse, favoring managed connectors like Lakeflow Connect where appropriate.
· Build and maintain Kimball-style dimensional models — facts, conformed dimensions, and slowly changing dimensions — as the analytics layer of record.
· Develop, test, and document transformations in dbt (models, sources, snapshots, tests, exposures) with strong CI discipline.
· Manage data assets in Unity Catalog, including catalogs, schemas, permissions, and lineage.
· Optimize performance and cost through cluster and warehouse sizing, Spark tuning, partitioning, and tagging for cost attribution.
· Operationalize machine learning workflows using MLflow for experiment tracking, model registry, and deployment, applying MLOps best practices.
· Help 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.
· Translate business and technical requirements into clear specifications, acceptance criteria, and design guidance that offshore teams can execute with minimal ambiguity.
· Quality-check offshore deliverables through code review, testing, and validation against data standards, performance targets, and definition-of-done before changes are promoted to production.
· Collaborates 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.
· Uphold engineering standards, code review practices, and documentation conventions across both onshore and offshore contributors.
· Support the team's growth by training and coaching engineers on tools, standards, and best practices as the platform scales.
What Success Looks Like
· Reliable, well-modeled data products that stakeholders trust and use without rework or manual reconciliation.
· Pipelines that run efficiently and cost-effectively, with issues caught proactively through monitoring rather than reported by downstream users.
· Machine learning models moved from experimentation into governed production with reproducible, monitored MLOps workflows.
· Offshore and vendor deliverables consistently meet quality and standards on first review, with minimal rework.
· Recognized as a technical lead others rely on, able to represent the team, and unblock engineers.
How Impact Will be Measured
· Data quality, pipeline reliability, and freshness SLAs met across owned datasets.
· Reduction in data incidents and in time-to-resolution for pipeline and reconciliation issues.
· On-time delivery of dimensional models and data products that unblock analytics and AI initiatives.
What You’ll Bring to the Team
Experience That Matters Most
· 7+ years in data engineering on big data and cloud platforms, including 3+ years hands-on with Databricks (Spark/PySpark, Delta Lake, jobs).
· Proven delivery of Kimball / dimensional data models in a modern warehouse or lakehouse, with strong SQL and Python (PySpark).
· Production experience with dbt (models, tests, snapshots) and with Unity Catalog for governance, access control, and lineage.
· Working knowledge of the ML lifecycle and MLOps, including MLflow for experiment tracking, model registry, and deployment.
· Experience translating business and technical requirements into clear specifications and coordinating or overseeing offshore and vendor engineering resources, including reviewing their deliverables for quality.
· Strong communication and stakeholder skills, with a track record of mentoring engineers and setting technical standards.
Experience That’s Great to Have
· Real-time / streaming experience (Structured Streaming, Kafka, or Azure Event Hubs) and familiarity with the Salesforce data model.
· Cost governance across multi-workspace Databricks environments — cluster policies, tagging, and system.billing.usage analysis.
· BI / visualization exposure (Power BI, Tableau, or Databricks dashboards/Genie) and containerization (Docker) for reproducible workflows.
· Prior technical-lead, team-lead, or technical-management exposure.
· Experience managing vendor or partner relationships, or distributed and offshore delivery models.
Risepoint is an equal-opportunity employer and supports a diverse and inclusive workforce.
Originally posted on Himalayas