Responsibilities
- Lead the team to improve scalability, reliability, and cost-efficiency of the Data Platform.
- Design, build, and deploy data pipelines (batch & streaming) using Spark orchestrated via Airflow.
- Develop libraries and frameworks for data ingestion, transformation, and governance with clean architecture principles.
- Collaborate with Data Architects to design/review data models, enforce data contracts, and maintain schema governance.
- Optimize performance with partitioning, caching, Z-Ordering, and metadata management in lakehouse environments (Delta/Iceberg/Hudi).
- Ensure security and compliance: IAM, encryption, secrets management, and GDPR/CCPA adherence.
- Drive CI/CD for data workflows, IaC (Terraform), and container orchestration (Kubernetes).
- Monitor SLOs/SLAs, implement alerting, and lead incident responses and postmortems.
- Design and operate end-to-end ML/LLM pipelines: data prep, training, evaluation, and deployment.
- Build RAG architectures, vector search, and embedding pipelines for LLM-based applications.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or a related technical field
- English is required
- Have 5+ years of experience as a Data Engineer or Software Engineer
- Have experience in Cloud (AWS/Azure/GCP)
- Extremely proficient in at least 1 programming language (Python/Scala/Java)
- Strong experience in systems architecture – particularly in complex, scalable, and fault tolerant distributed systems• Good at multi-threading, atomic operations, computation framework: Spark
- (DataFrame, SQL, ...), distributed storage, distributed computing
- Understand designs of resilience, fault-tolerance, high availability, and high scalability, ...
- Tools: CI/CD, Gitlab, ...
- Good at communication & team working
- Being open-minded, willing to learn new things
HOW TO APPLY: Please send your CV to the consultant in charge:
E-mail: uyen.nguyen@ev-search.com
All applications will be considered without regard to race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, parental status, military service, or any other non-merit factor.
