Senior Data Platform Engineer

Your Day to Day

• Own the full data platform — ingestion, modeling, self-service, CI/CD, and visualization — including the process, documentation, and enforcement that keeps standards followed.
• Design and build best-in-class reliable, scalable, and performant data architecture and data flows to support the democratization of data, analytics, AI, and ML initiatives.
• Partner with Software to identify, recommend, and implement the right tech stack to support streaming data at scale that integrates into our existing stack: Fivetran, Snowflake, dbt, Looker, Grafana.
• Own data modeling, transformation, and orchestration for IOT — machine sensors, digital UI interactions, and beverage consumption — partnering with Software, Hardware, Product, and Operations to translate complex data needs into production-ready solutions.
• Build the governed self-service model that lets teams build and ship their own data work safely; includes permissioning, PR-based peer review, production-readiness standards, and full lineage/traceability from source to dashboard.
• Continue to evolve how AI connects to our data including the governance and guardrails that let AI operate at scale without sacrificing accuracy.
• Continue to evolve the monitoring and alerting process so that it is scalable and reliable; comprehensive with minimal noise.
• Define and enforce best practices for privacy, governance, and security — including how we handle sensitive data (e.g., HR/people data) — and build the audit-ready rigor and change-management discipline a fast-scaling company needs.
• Collaborate with Data Science team members to enable advanced analytics, experimentation, and AI/ML modeling.
• Provide technical leadership to engineers across the Data & Data Science organization.
• Stay current with emerging data technologies and recommend strategic improvements to the data platform.

Who You Are

• 8+ years of experience in data engineering, analytics engineering, or platform engineering, with demonstrated ownership of a full production data platform (not just a slice of it).
• Expert with modern cloud data stack tooling: Fivetran, Snowflake, dbt, and a modern BI layer (Looker or similar).
• Fluent in how AI tools consume data — designed or governed access patterns for LLMs/AI agents querying structured data (eg semantic layers, read-only access controls, tool-calling patterns).
• Deep, hands-on experience architecting streaming and batch data pipelines at scale (e.g., Kafka, Kinesis, Spark, InfluxDB).
• Track record designing governance models that let non-engineers quickly and safely self-serve — permissions, PR/review workflows, production-readiness gates.
• Experience building the observability layer for a data platform — monitoring, alerting, lineage, and data quality tracking — so breaks get caught immediately and every dataset can be traced from source to consumption.
• Strong SQL and Python, with a demonstrated track record of independently driving ambiguous, cross-functional problems all the way to production — not just recommending a solution, but building and shipping it yourself.
• Track record of mentoring engineers and driving innovation within high-performing data teams.

Compensation & Benefits

Pay Range: $170,000—$210,000 USD

Benefits:

• Comprehensive medical, dental and vision insurance plans with BlueCross BlueShield, 95% paid by employer
• 401(k) with company match
• Flexible PTO plus 12 company holidays, and additional paid days for sick leave, etc
• Generous fully paid parental leave for both birth parents and non-birth parents
• Fully employer paid disability and life insurances
• Wellness and fitness reimbursements
• Monthly stipends for cell phone use and commuting costs
• Onsite snacks, weekly catered lunch, and (of course) unlimited Bevi ... plus composting and terra-cycling, too
• Happy hours, team-building events, bagel breakfasts, Values awards - and more.