Senior Analytics Engineer
At Instructure, we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers.
We do this by giving smart, creative, passionate people opportunities to create awesome. And that's where you come in:
We're hiring a Senior Analytics Engineer to build the data models that teams across Instructure use to run the business. You'll work in dbt and Snowflake every day. You'll also sit in the room with business leaders, understand the decisions they're making, and design data that answers their questions correctly the first time.
This role is broad by design. You'll support analytics across Finance, Operations, Product Management, Sales, and other parts of the company, so you'll see how the whole business fits together. It suits someone who writes clean SQL and also asks "what is this number used for?" before building it. You'll own models from source to dashboard, and you'll help set the standards for how we design, test, and document our data.
What you'll do
Design, build, and maintain dimensional models in dbt and Snowflake that support reporting and analysis across Finance, Operations, Product Management, and other business functions.
Translate business questions into clear data requirements by working directly with senior leaders across the company.
Own the full model lifecycle: source ingestion patterns, staging, intermediate and mart layers, tests, documentation, and deployment.
Set and enforce modeling standards, including naming conventions, grain definitions, slowly changing dimension handling, and testing practices.
Tune Snowflake performance and cost through smart materialization choices, clustering, and warehouse sizing.
Partner with data engineers and business systems teams to make sure upstream data arrives in a shape we can model well.
Review pull requests, mentor analysts and engineers, and raise the quality bar across the team.
Explain data definitions and tradeoffs to non-technical audiences in plain language, and push back when a request would produce a misleading metric.
What you bring
5+ years of experience in analytics engineering, data engineering, or a similar role.
A bachelor's degree in Computer Science, Information Systems, Finance, or another quantitative field is preferred.
Deep, hands-on experience with dbt, including macros, packages, incremental models, snapshots, and testing.
Strong Snowflake skills, with advanced SQL and a good sense of performance and cost.
Proven experience designing dimensional models (star schemas, facts, dimensions, conformed dimensions) and a clear point of view on when to use them and when not to.
Solid data architecture instincts: layered modeling, grain, lineage, data contracts, and maintainable project structure.
Working knowledge of how a business operates across functions such as finance, sales, operations, and product, ideally in a SaaS company.
The ability to hold a credible conversation with directors, VPs, and executives about business issues, and to turn that conversation into a data solution.
Strong written and verbal communication. You document your work and explain it well.
Experience with Git-based workflows and CI/CD for data projects.
Nice to have
Experience modeling data from CRM and other business systems such as Salesforce.
Familiarity with orchestration tools such as Airflow or dbt Cloud.
Experience with a BI tool such as Tableau, Looker, or Power BI.
Background in a SaaS or education technology company.
What success looks like
In 90 days, you've shipped your first production models and built working relationships with partners in several business functions.
In 6 months, you own a major domain in our data model and the business trusts the numbers it produces.
In a year, you've shaped our modeling standards and made the whole team faster.