Data Infrastructure Engineer-Spain
Primary stack
Nice to have's
Job description
About the Position
Data Infrastructure Engineer-Spain
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.
Responsibilities
- 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.
Requirements
- 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
We Offer
At Doodle, we're committed to an environment of mutual trust and respect, where equal employment opportunities are available to all applicants and teammates without regard to age, race, ethnic origin, color, disability, religion or belief, gender, gender identity, or sexual orientation. Diversity and inclusion are of utmost importance to us. We're committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work and our products will be. We want to hear from you, so please don't hesitate to apply. We look forward to receiving your application.
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.
© Doodle. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.
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