Job Description
Key Accountabilities
• 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.
Success Profile - Qualification and Experiences
• 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
• Experience with Databricks (Delta Lake, Unity Catalog, Delta Live Tables) or similar lakehouse technologies is a strong plus.
• Proven ability in performance tuning and optimization for Big Data workloads (Spark/Flink, partitioning, shuffle strategies, caching).
• Familiarity with modern data transformation frameworks (dbt).
• Experience in AI and LLM technologies is a plus, including prompt engineering, embeddings, and retrieval-augmented generation (RAG).
• Hands-on experience with vector databases (ChromaDB, Vector Search) and LLMOps
practices.