Analytics Engineer

We're looking for a builder - an Analytics Engineer who thinks as much as they execute. You'll juggle a high-velocity mix of ownership across dbt models, Snowflake semantic layers, and data pipelines spanning finance, operations, IOT, and marketing - and you'll build net-new ingestion for data sources that don't have a structured feed yet, including the AI-powered data products we're building next. What sets this person apart is judgment - someone who looks at how something's done today and pushes it to be better, brings their own point of view rather than just taking the ask at face value, and builds things that hold up rather than just ship.

• Maintain the dbt models and Snowflake semantic layers that power core reporting across finance, operations, IOT, and marketing - keeping them tested, documented, and trusted by the team.
• Own the marketing data pipeline end-to-end: build the dbt models and data engineering needed to bring marketing data in, including creatively designing new processes to capture non-traditional data streams that don't yet have a structured feed.
• Root-cause and resolve complex data issues anywhere in the pipeline - data gaps, migration mismatches, permission changes, broken alerts - especially the ones without an obvious fix, and use what you find to make the pipeline more resilient, not just patched.
• Partner directly with cross-functional teams, translating ad hoc data requests into scoped, well-understood problems - bringing your own recommendation on the best way to solve it, not just executing the ask as stated.
• Contribute to new AI-powered data products, building semantic layers and data models that make company data usable by AI agents as well as people.
• Own data ingestion from multiple systems (e.g., Salesforce, NetSuite, Zendesk, ADP), including handling sensitive data with appropriate care.
• Bring ideas for how the team's data models, testing, and documentation could be better — partnering with the Data Platform team member who owns and enforces the standards to help make improvements stick.

• 4-6 years of professional experience in analytics engineering, data engineering, or business intelligence. BS in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field (MS a plus, not required).
• Expert in dbt and SQL, with strong knowledge of Snowflake or other cloud data warehouse.
• Experienced with data engineering - building ingestion pipelines that bring new data sources in, not just transforming data that's already landed.
• Comfortable building pipeline monitoring and alerting (eg freshness checks, data quality alerts) - elevated pipeline engineering, not just basic ETL.
• Experienced translating ambiguous, loosely-defined requests into scoped, well-understood problems and delivery plans.
• Track record of proactively identifying and driving improvements to tooling, models, or process - not just executing what's assigned.
• Reputation for thorough, dependable work that's trusted by the team.
• Skilled at communicating technical work clearly to technical and non-technical audiences.
• Sound judgment on trade-offs, with a track record of flagging risks early.

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Bevi is committed to an inclusive hiring process and we aim to provide accommodations for persons with disabilities. If you need any accommodations for the application or throughout the interview process please contact recruiting@bevi.co.