Lead Data Engineer - Backend (Python / Typescript / Big Data / AWS / Kubernetes)
New
V
Varicent Sales Performance Management
Mexico, Remote, Eastern Standard Time (EST)Full-TimeLead
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 7+ years
- Required Skills
- AWSNode.jsPythonKafkaKubernetesTypeScriptSparkCI/CDRESTful APIsTerraformMicroservicesDistributed Systems
Requirements
- 7+ years of backend or full-stack engineering experience with a strong backend focus.
- 7+ years of hands-on Python experience (APIs, automation, large-scale data pipelines).
- 3+ years working with Typescript / Node.js.
- Advanced experience with AWS (EC2, EKS, Lambda, S3, DynamoDB, RDS, Step Functions, etc.).
- Strong practical experience deploying and optimizing production workloads on Kubernetes.
- Proven experience working with large datasets, distributed computing, and batch or stream processing using Spark, Dask, EMR, Glue, Kafka, etc.
- Solid understanding of system design, distributed systems, scalability patterns, and cloud architecture.
- Strong business-level English proficiency to communicate effectively with cross-functional teams across regions.
- Ability to collaborate, document solutions clearly, and participate in technical discussions with stakeholders.
Responsibilities
- Design, build, and scale new features for REST APIs and large-scale data processing pipelines that handle high-volume datasets across distributed systems.
- Architect and optimize backend services for high throughput and low-latency performance.
- Develop data-intensive and event-driven applications using Python, Typescript, Spark, and AWS-native services.
- Work with Spark, EMR, Glue, Kafka, or similar frameworks to process and transform very large datasets.
- Improve system performance, reliability, and scalability across microservices and cloud infrastructure.
- Partner with senior engineers, architects, DevOps, and QA throughout the full development lifecycle.
- Mentor developers, guide code reviews, and raise engineering quality standards.
- Automate deployments and CI/CD using Terraform, Serverless Framework, and Kubernetes-based workflows.
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