Data Infrastructure & MLOps Engineer (all genders)

Doodle
Berlin
Workplace: HybridFull timeFunction: DevOps, Cloud & InfrastructureSkills: ["Communication","Collaboration","Reliability focus","Incident investigation","Security-conscious approach"]

Own the reliability, scalability, and automation of the data and machine learning platforms behind Doodle’s products. Design and operate scalable data infrastructure and batch/streaming pipelines, establish platform standards, and improve data discoverability. Build and run production MLOps workflows (training, versioning, deployment, monitoring, drift detection, and incident response) while managing cloud infrastructure via infrastructure-as-code and automated deployments. Partner across engineering, data science, security, and operations to deliver secure, governed, observable systems.

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Doodle
Doodle
1 day ago

Data Infrastructure & MLOps Engineer (all genders)

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Last checked: 2 hours agoStatus: Live

Job Summary

Own the reliability, scalability, and automation of the data and machine learning platforms behind Doodle’s products. Design and operate scalable data infrastructure and batch/streaming pipelines, establish platform standards, and improve data discoverability. Build and run production MLOps workflows (training, versioning, deployment, monitoring, drift detection, and incident response) while managing cloud infrastructure via infrastructure-as-code and automated deployments. Partner across engineering, data science, security, and operations to deliver secure, governed, observable systems.
Location: Berlin
Workplace: Hybrid
Employment Type: Full time
Job Function: DevOps, Cloud & Infrastructure

Key Responsibilities

  • •Design, build, and operate scalable data infrastructure for ingestion, transformation, storage, and serving.
  • •Develop reliable batch and streaming pipelines for product analytics, BI, and ML use cases.
  • •Build and maintain MLOps workflows for experimentation, data/model versioning, training, evaluation, deployment, and rollback.
  • •Operate ML workloads in production, including model serving, feature pipelines, scheduled retraining, inference infrastructure, and monitoring/drift detection.
  • •Manage cloud infrastructure and automation using infrastructure as code; define SLOs, runbooks, alerts, and lead incident root cause analysis and improvements.

Key Requirements

  • •Professional experience in data engineering, platform engineering, MLOps, DevOps, or a closely related role.
  • •Strong Python and SQL skills, building production-quality software and data pipelines.
  • •Hands-on experience with cloud infrastructure, containers, CI/CD, and infrastructure as code.
  • •Experience with data warehouses, data lakes, workflow orchestration, and batch or streaming processing.
  • •Practical knowledge of the ML lifecycle (deployment, monitoring, and reproducibility) and observability/incident management.
Skills:CommunicationCollaborationReliability focusIncident investigationSecurity-conscious approach
Tech Stack:PythonSQLCI/CDInfrastructure as codeContainersKubernetesTerraformAirflowDbtSparkKafkaMLflowFeature storesVector databasesLLM applicationsRetrieval-augmented generationAgentic AI

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Doodle
Provides an online scheduling platform that simplifies meeting coordination by allowing users to propose times, vote on availability, and automate calendar bookings across teams and external participants, aimed at improving scheduling efficiency for businesses and individuals.
Industry: Enterprise Software
Company Size: Medium (51 to 250 employees)
Growth: Established Company
Headquarters: Zurich, Switzerland
Founded: 2007
WebsiteLinkedIn