[Hiring] Analytics Engineering Co-Op @Risepoint

Role Description

We're investing deliberately in the transformation layer: standardized semantic layer components, clear metric definitions, and documented models that hold up as the business grows. This co-op is a real seat in that work and carries with it the opportunity to make material impact on strategy and business outcomes.

You'll sit between analysis and engineering: building and standardizing the data models and semantic layer components that let our analytics leaders ship validated, governed data products faster.

This role will serve as a working liaison between Student Success Center (SSC) Analytics and our Business Technology team. You will translate what the business needs into something technically sound and communicate what's possible back into something operators can act on. This role provides you with the opportunity to work cohesively between the data and analytics layer.

Co-op Duration

• This opportunity runs from January 2027 through June 2027, with some flexibility on start and end to accommodate academic schedules.

Weekly Schedule

• This is a full-time co-op experience, 40 hours per week, Monday through Friday, designed to provide hands-on, real-world exposure.

What You Will Do

• Design and build data models in Databricks that consolidate scattered logic into a defensible source of truth for our student success center (SSC) reporting and analysis.
• Standardize semantic layer components and metric definitions so operational metrics mean one thing in every report, dashboard, and leadership deck.
• Improve data quality at the transformation layer — validation, freshness, and reconciliation checks, so we find problems before a leader does.
• Partner with Business Technology on upstream data sources, pipeline changes, and governance, acting as the connective tissue between the two teams.
• Turn a recurring manual report into a modeled data product in Power BI that maintains itself, then quantify the hours you gave back.
• Document what you build — lineage, definitions, and the reasoning, so it's usable by someone who wasn't in the room. Contribute new documentation for existing deliverables, ensure consistency across documentation, and simplify the overall approach while retaining context.
• Recommend, don't just execute. When you see a better way to model something, we want to hear it, and we'll give you room to try it.

What Success Looks Like

• Two or three core SSC data models are built, documented, and trusted enough that analysts query them instead of rewriting the logic themselves.
• Contribute meaningfully to our semantic layer for our most used operational metrics.
• Coordinate with stakeholders in SSC to enable better workflows, drive programmatic analytics solutions, and serve as a liaison for our SSC Analytics team.
• Adoption: how many reports and dashboards are rebuilt on the models you standardized, versus still running on one-off logic.
• Hours returned: manual report building and rework time eliminated, measured against a baseline we set in your first month.
• Net improvement of our Databricks environment specific to the catalogs and schemas that we own and manage. Measured by user feedback and adoption.

What You’ll Bring to the Team

Experience That Matters Most

• Working SQL ability. You can write joins and aggregations, follow someone else’s query, and are confident with debugging and resolving open data issues within queries, notebooks, and jobs.
• Functional Python experience. We do not require you to have advanced proficiency with Python, but have the confidence to navigate existing scripts, workflows, and understand how to approach and implement solutions with the help of AI.
• Foundational database management concepts. Keys, grains, normalization, and a basic sense of what makes a table well designed and structured.
• Strong analytical judgment and experience in a real-world business setting.
• Demonstrated experience working with ambiguity to drive measurable outcomes to stakeholders.
• Strong verbal and written communication skills across both technical and non-technical stakeholder cohorts.

Experience That’s Great to Have

• Prior experience working within a data engineering and/or analytics environment alongside others.
• Databricks, Snowflake, or any cloud data platform application knowledge and experience.
• Confidence with technical documentation and overall project management within an engineering framework.
• Applicable knowledge working with data transformation tools, application and confidence with AI-tooling in an analytics workflow, and overall confidence navigating in and out of engineering workflows.