The role
- COMP
- $180K - $320K
- EQUITY
- Competitive Equity
- LOCATION
- Canada
- WORKPLACE
- Remote
- EXPERIENCE
- 7+ years
- VISA
- None, Visa transfers
- STACK
- Kafka, Kubernetes, Docker, Spark
- INDUSTRY
- B2B, Data, Marketing, AI, Enterprise
/roles — ROLE_178
Software Engineer, Distributed Systems (Core)
Late-stage data activation platform moving warehouse customer data to hundreds of marketing and business tools
The company
A late-stage data and AI company whose platform lets businesses activate customer data from their own warehouse for marketing, advertising, and personalization, increasingly through AI agents.
- STAGE
- scale-up
- FUNDING
- $320M+ raised
- TEAM
- 350+ people
- FOUNDED
- 2018
- BACKING
- backed by Y Combinator, Goldman Sachs
JD — the work
About the role
You would work on the syncing engine at the center of the product, the system that customers and other internal teams depend on to move very large volumes of customer data. The job calls for serious distributed systems depth along with performance tuning and debugging skill, applied to infrastructure that spans several clouds and regions for customers worldwide. Engineers get a lot of ownership here: you would drive projects end to end, partner with customers on their hardest scaling challenges, and help decide what the team takes on next.
What you'll do
- Profile each stage of the sync pipeline so huge data volumes reach major ad platforms faster
- Add real-time streaming alongside today's batch-only syncs, with inputs such as queues and webhooks
- Spot upcoming scaling and reliability bottlenecks and design for roughly ten times today's load
- Improve a low-latency caching API that serves warehouse data for real-time personalization at very high request rates
- Grow the multi-region, multi-cloud backend into new regions to meet data residency needs
What they're looking for
- 7+ years of software engineering experience
- Deep distributed systems knowledge, plus strong performance tuning and production troubleshooting
- Comfort owning projects end to end and working directly with customers
- Experience with infrastructure such as Kafka, Kubernetes, Docker, or Spark