Senior Data Engineer

Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. As a Senior Data Engineer on the Enterprise Data Platform team, you will build and operate governed data products that power analytics, reporting, and AI/ML for the universities and colleges Risepoint supports.

Responsibilities:

• 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

Requirements:

• 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
• 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