Data Infrastructure Engineer
Posted Updated
- 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
- 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
- 2+ years in data platform engineering or scalable software systems
- Redshift and production Airflow: data modeling and performance tuning
- Kafka and Avro: schema design, serialization and evolution
- Clear communication with both technical and non-technical stakeholders
- Cloud and DevOps: CI/CD, infrastructure as code and Kubernetes, plus data lakes and open table formats
- Dbt: experience with it is a plus
- Reliability and security: observability, incident response and debugging pipeline failures, with a security-conscious approach to data access, privacy and secrets
