About the role
Doodle is a B2B SaaS platform used by millions of professionals to coordinate meetings and collaborate across teams and companies. We're building the future of scheduling and productivity, working on hard problems where human collaboration meets digital efficiency. We craft elegant solutions to complex problems, and we're looking for passionate problem solvers who share that drive.
Our product, engineering and analytics teams rely on data every day to decide what we build next. As our Data Infrastructure Engineer, you'll bring data engineering and infrastructure together, designing, building and running the platforms that keep our data reliable, secure and easy to use. You'll work closely with our SRE, product, engineering, analytics and security teams to make data simpler for everyone.
Why join us
•Real impact: your commits change how the world meets.
•Technical stretch: we deal with "time" problems most companies never meet. Our systems are high-throughput, distributed and reactive by design. It's a masterclass in building systems around a moving target.
•Modern tooling: we give you the best AI and agentic tools, and we expect you to use them.
•Culture of trust: we value curiosity, autonomy, and a balance that respects your time as much as our users.
What you'll do
•Build the platform: design, build and operate scalable infrastructure for data ingestion, transformation, storage and serving. Run streaming flows with Kafka and Avro, orchestrate batch workflows in Airflow, and keep Redshift performant, well-modeled and cost-effective.
•Run infrastructure as code: manage secure, reproducible cloud environments for development, testing and production, with sensible cost controls.
•Keep it reliable and secure: own SLOs, runbooks and alerts for critical data systems. Protect sensitive data through access controls, encryption, secrets management and retention policies.
•Fix problems at the root: investigate incidents, lead root-cause analysis and implement preventative improvements.
•Make data easy to use: improve discoverability through cataloging, lineage, ownership and quality testing, and build documentation, tooling and self-service workflows so analytics and engineering teams can work independently.
What you bring
•2+ years in data platform engineering or scalable software systems
•Kafka and Avro: schema design, serialization and evolution
•Redshift and production Airflow: data modeling and performance tuning
•Cloud and DevOps: CI/CD, infrastructure as code and Kubernetes, plus data lakes and open table formats
•Reliability and security: observability, incident response and debugging pipeline failures, with a security-conscious approach to data access, privacy and secrets
•Clear communication with both technical and non-technical stakeholders
•dbt: experience with it is a plus
Important notice
We can only consider applications from candidates who are based in, and have the right to work in, the country where this role is advertised. At this time we are unable to sponsor a visa or support relocation for this position.
