About the Company
At Camlin we pride ourselves on designing and building complete solutions in-house. We create everything from hardware PCB designs to device firmware, Linux drivers, IoT application software, server software, server web UIs, mobile apps, and machine learning and data science solutions. We believe that by controlling every aspect of the development process, we can deliver truly unique and exceptional products to our customers.
Responsibilities
•Design, build, operate, and continuously improve our cloud-native data platform and data pipelines.
•Work closely with data engineers, AI and analytics teams and platform stakeholders to develop scalable, secure, and resilient data infrastructure following Infrastructure as Code and DevOps best practices.
•Participate in selection and adoption of technologies for Big Data processing, frameworks and processes so it can be standardized and adopted across multiple projects.
•Be involved in both the discovery and delivery phases of development projects, ensuring that the correct technical strategies are considered early in the product development and take a hands-on approach to ensure that the projects are delivered on-time to high-quality.
•Drive the development and adoption of reusable frameworks components and provide mentorship to all software and data engineers to ensure consistency of implementation between project teams.
•Design, implement, and support scalable data platform solutions on AWS.
•Develop and optimize data processing workloads using Apache Spark and Databricks.
•Build and maintain streaming data solutions using Apache Kafka and Amazon MSK.
•Manage cloud infrastructure using an Infrastructure as Code (IaC) approach, with a strong focus on automation and repeatability.
•Develop and maintain CI/CD pipelines to ensure reliable deployment of platform components and data workloads.
•Implement security best practices, including identity and access management controls.
•Design and support Disaster Recovery (DR) and business continuity strategies for critical platform services.
•Monitor platform health, performance, and reliability, proactively identifying areas for improvement.
•Collaborate with engineering teams to improve platform scalability, observability, and operational excellence.
Requirements
•3+ years of experience in Data Engineering or Data Platform engineering roles
•Strong knowledge of Apache Spark
•Experience working with Databricks for large-scale data processing and analytics
•Good knowledge of AWS services, including S3, AWS IAM, EC2, RDS and MSK
•Understanding of cloud architecture, security, and operational best practices
•Strong understanding of Apache Kafka concepts, architecture, and operations
•Experience designing and supporting event-driven and streaming data solutions
•Proficiency in Terraform for infrastructure provisioning and management
•Strong commitment to an Infrastructure as Code First approach
•Experience developing and maintaining CI/CD pipelines using modern DevOps tools and practices
•Solid proficiency in Python for automation, platform engineering, and data processing tasks
•Understanding of Disaster Recovery, backup strategies, high availability, and business continuity planning
•Fluency in English, written and verbal
Nice to Have
•Bachelor's degree in computer software engineering or equivalent
•Experience building SaaS applications
•Knowledge of secure software development principles
•Experience with Datadog for monitoring, observability, and alerting
•Experience operating data platforms in production environments
•Knowledge of platform monitoring, capacity planning, and operational excellence practices
Conditions
No conditions specified in the posting.
