Head of Data Engineering

About Raise Loyalty
Raise is building an AI-native loyalty platform that helps brands acquire customers, increase repeat purchases and bring inactive customers back. AI helps teams plan campaigns, recommend offers and measure results, with people setting budgets and approving campaigns.
We’re building and testing the product within Raise, drawing on our established commerce, payments and gift card business. You’ll work directly with the CTO and product stakeholders to build reliable data systems that support the business.
The role
Own the architecture, development and operation of Raise’s data platform. You’ll connect operational and customer data, establish trusted datasets and make that data usable across engineering, product, finance and AI-powered applications.
This is a hands-on individual contributor role with no direct reports. You’ll spend most of your time designing, coding, testing and operating production systems, with responsibility for technical decisions and delivery.
What you’ll do
Design and build data architecture that balances reliability, performance and cost.
Develop and maintain integrations across payment platforms, CRMs, analytics tools and operational databases.
Build dependable batch and streaming pipelines with monitoring, automated quality checks and recovery capabilities.
Create consistent data models and clearly defined metrics that teams can trust.
Establish secure access, sensitive-data controls, documentation and data lineage.
Work with product, finance and AI engineers to deliver reliable datasets for analytics, experimentation and machine learning.
Diagnose production issues, improve query performance and keep systems maintainable as usage grows.
What you bring
Eight or more years of relevant data engineering or data platform experience, with a track record of building and operating production systems.
At least two years owning a production data platform, including architecture decisions, implementation and operational reliability.
Strong SQL and programming skills, with the ability to write production code and troubleshoot complex data problems.
Deep experience with analytical databases, data modeling, query optimization and changing or late-arriving data.
Experience with API integrations, change data capture, batch and streaming ingestion, and source-to-target reconciliation.
Practical knowledge of observability, automated testing, incident recovery and data security.
The ability to work independently, explain technical trade-offs and translate business needs into dependable systems.
Experience with ClickHouse, commerce or payments data, transformation tooling, identity resolution and experiment instrumentation is valuable.
How we work
You’ll work directly with the CTO to agree priorities and make technical decisions, collaborating closely with engineering, product and finance.
We value practical architecture, clear ownership and reliable delivery. You should be comfortable moving between system design and the code required to make it work.
Apply
Share your résumé and examples of data systems you’ve built and operated. Tell us what you owned, a difficult production problem you resolved and how you improved reliability, performance or cost.

Work arrangement
Yes