Senior Data Platform Engineer

Role Description

We're looking for a Senior Data Platform Engineer to own the foundation the entire Data & Data Science org builds on. This is a senior individual-contributor role — reporting directly to our Head of Data — that's both strategic and deeply hands-on, spanning the full data platform stack from Git-based build standards through ingestion, transformation, and self-service BI. You'll work alongside other senior technical leaders on the team, each owning deep technical domains of their own.

Your proving ground: designing and building our IoT data model — machine sensors, digital UI interactions, and beverage consumption — end to end, hands-on. It's a real, complex system, and it's how you show you can build on the platform you're setting standards for, not just describe best practices. You'll design and build the architecture and governance that let a growing team of analytics engineers, data scientists, and AI tools build and ship safely and independently, at speed, without sacrificing accuracy.

This isn't a role that writes a playbook and hands it off: you'll build the platform yourself, evolve what the team has already put in place, and be the person other engineers look to for how it's done right. The right candidate combines deep technical expertise with innovation and operational excellence, thriving in a hands-on role to deliver scalable, production-ready, high-impact data solutions.

Qualifications

• 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 (e.g., 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.

Requirements

• 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.

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.