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Jobs(27,731)/Analytics Engineer Role(110)/instructure (14) - Senior Analytics Engineer
instructure

instructure - Senior Analytics Engineer

US-REMOTE - Hybrid2d ago
In OfficeSeniorNAMental HealthSoftwareAnalytics EngineerSenior Data ScientistLearning & DevelopmentdbtSnowflakeSQLGitSalesforceAirflowTableauLookerPower BIReportingDocumentation

Requirements

• 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. • 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. • Get in on all the awesome at Instructure! • We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here's a general idea of what you can expect: • Competitive compensation, plus all full-time employees participate in our ownership program - because everyone should have a stake in our success. • Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location. • Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs. • Comprehensive wellness programs and mental health support • Learning and development resources, including professional development tools and tuition reimbursement, to support your growth • The technology and tools you need to do your best work • Motivosity employee recognition program • A culture rooted in inclusivity, support, and meaningful connection • We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.

Responsibilities

• 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.

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