At Doodle, this engineer designs and runs the data and ML platform: building batch/streaming data pipelines for analytics, BI and ML, and owning MLOps workflows like model deployment, monitoring, retraining and rollback. Core tech includes Python, SQL, cloud infrastructure, containers, CI/CD, IaC, data warehouses/lakes and workflow orchestration in a SaaS environment.
Experteer Overview
Las cualificaciones, habilidades y toda la experiencia relevante necesaria para este puesto se pueden encontrar en la descripción completa a continuación.
As a Data Infrastructure u0026amp; MLOps Engineer at Doodle, you design and run the platforms that enable data engineering, analytics, and machine learning across the product. You’ll partner with product, engineering, data, and security teams to improve data accessibility, model deployment, and system reliability. You’ll own the data and ML platform’s reliability, scalability, and automation, shaping a secure, observable, and cost-aware data ecosystem. This role offers impact across product analytics, BI, and ML use cases within a growing SaaS environment.
Compensaciones / Beneficios
• Design, build, and operate scalable data infrastructure for ingestion, transformation, storage, and serving
• Develop reliable batch and streaming data pipelines for product analytics, BI, and ML use cases
• Establish data platform standards for performance, availability, observability, documentation, and cost management
• Improve data discoverability and usability via data cataloguing, lineage, ownership, and quality processes
• Build and maintain MLOps workflows covering experimentation, data/model versioning, training, evaluation, deployment, and rollback
• Operate ML workloads in production, including model serving, feature pipelines, retraining, and inference infrastructure
• Collaborate with data scientists and software engineers to turn prototypes into production services
• Introduce repeatable approaches for model validation, monitoring, drift detection, performance measurement, and incident response
• Manage cloud-based data and ML infrastructure with IaC and automated deployments
• Create secure, reproducible environments for development, testing, and production
• Enhance platform efficiency through automation, capacity planning, resource optimisation, and cost controls
• Contribute to platform architecture decisions and evolve technical foundations as business grows
• Define SLAs, runbooks, alerts, dashboards, and on-call processes for data/ML systems
• Protect data with access controls, encryption, secrets management, retention policies, and secure development practices
• Support compliance, privacy, and responsible AI through traceability and documentation
• Investigate incidents, perform root-cause analysis, and implement preventative improvements
• Collaborate with product, engineering, analytics, data science, security, and operations teams to deliver platform solutions
• Create documentation, reusable tooling, and self-service workflows for team autonomy
• Contribute to engineering xqbhyrx standards, planning, code reviews, and knowledge sharing
Responsabilidades
• Professional experience in data engineering, platform engineering, MLOps, DevOps, or related role
• Strong Python and SQL skills with production-quality data pipelines
• Hands-on experience with cloud infrastructure, containers, CI/CD, and IaC
• Experience with data warehouses, data lakes, workflow orchestration, and batch/streaming processing
• Practical knowledge of ML lifecycle management, deployment, monitoring, and reproducibility
• Experience with observability, incident management, reliability, and performance optimization
• Security-conscious approach to data access, privacy, secrets management, and production ops
• Strong communication skills to explain decisions to technical and non-technical stakeholders
Requisitos principales
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