Job 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. 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.

Requirements

Must-haves

The Impact You Will Make
How You Will Bring Our Mission to Life
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningsqlpythonazuredocker

Platform: workday
Effort: long