Data Infrastructure Engineer

DoodleBerlin

Stellenbeschreibung:

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

Reliability and security: observability, incident response and debugging pipeline failures, with a security-conscious approach to data access, privacy and secrets
Dbt: experience with it is a plus
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
Kafka and Avro: schema design, serialization and evolution
Redshift and production Airflow: data modeling and performance tuning
2+ years in data platform engineering or scalable software systems

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