Engineering Manager - Data Engineering & Insights
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IT/Tech
Data Engineering, Data Science Manager
Job Description & How to Apply Below
On-site in Seattle, WA, five days a week.
Who we areIn the past, to be a successful restaurateur, you simply had to have a passion for food and a passion for people - but to succeed as a digital restaurateur you also need to have a passion for technology. We believe in the joy of serving others, and that's why we created Otter – to help restaurateurs succeed in online food delivery.
Restaurants around the world, both large and small, including Chick-fil-A, Ben & Jerry’s, KFC, and Eataly trust our software to power their delivery business. We increase sales, reduce order issues, and decrease delivery headaches.
We are looking for a hands-on Engineering Manager to spearhead our evolution from traditional analytics to a next-generation intelligence platform. This is a great opportunity to lead a team managing the data behind roughly 80% of all online food orders in the US, and modernize our ecosystem by building an impeccable data foundation that moves beyond static charts into live, conversational insights.
TheImpact:
Empower merchants with fast, trustworthy, and actionable metrics that change how they run their businesses every single day.
What you’ll do- Own Otter Insights End-to-End: Lead the strategy for everything inside Business Manager (pipelines and datasets to the semantic modeling layer and customer-facing embedded analytics).
- Manage Ingestion to Serving: Drive both real-time and batch pipelines, ensuring canonical datasets and robust APIs are built to empower other internal teams.
- Scale the Semantic Layer: Oversee frameworks (like Cube/SQLMesh) to ensure metrics and dimensions remain consistent across batch and real-time processing.
- Drive Data Governance: Establish analytics engineering standards and ensure adoption across broader product teams to maintain high data quality.
- Ship High-Performance Products: Deliver data-intensive, customer-facing applications including the Analytics App, Analytics Studio, automated reports, and data-sharing APIs.
- Deploy Safe AI Insights: Build automated AI insights on top of governed metrics, implementing strict guardrails, observability, and customer-impact triage.
- Build and Coach the Team: Hire, mentor, and retain a cross-functional team of analytics engineers and full-stack/frontend engineers while setting technical direction across the stack.
- Blended Team Leadership: 4+ years of experience leading engineering teams across both data/platform and product domains
- Cross-Functional Team Building: Proven track record building and scaling diverse teams (backend, analytics engineering, and full-stack/frontend)
- Modern Data Modeling: A strong background in data modeling and semantic layers, alongside a history of delivering B2B or customer-facing analytics products
- Pragmatic Data Governance: Experience implementing data lineage, catalogs, compliance, and quality frameworks
- Product & Executive Partnership: Ability to collaborate closely with PMs, designers, and executive stakeholders w
- Full-Stack Accountability: Comfort owning end-to-end operational health, monitoring incidents, and managing on-call rotations from the data pipeline down to the user interface.
- Forward-Looking Mindset: A genuine interest in establishing AI-ready data foundations and building AI-enabled insights on top of certified metrics and metadata.
- Hands-on experience with SQLMesh, dbt, and Cube.
- Pipeline CI/CD, data observability, and orchestration (Flink / or similar batch tools)
- Click House, Pinot, or Trino
- Data access controls, platform hardening, and permissioned sharing
- Engineering experience across React, Type Script, and Java (or Kotlin/Scala)
- Building dashboards, custom…
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