ABOUT CLIENT
Our client is a startup company focusing on building a marketplace for insurance products
JOB DESCRIPTION
Designing and implementing scalable data architectures aligned with business objectives
Building and maintaining data pipelines for ingestion, transformation, and delivery using modern orchestration tools
Architecting data solutions across data warehousing, data lakes, and real-time analytics
Creating and maintaining data models and documenting the enterprise data landscape
Building and maintaining data infrastructure supporting ML model training, deployment, and monitoring
Designing and implementing vector database solutions for AI-powered features
Developing data pipelines for AI-driven capabilities and ensuring global scalability
Implementing DataOps practices to ensure data quality, lineage, and governance
Defining and enforcing data strategy and architectural principles
Building monitoring and alerting for pipeline health, data quality, and SLA compliance
Optimizing query performance and cost efficiency
Collaborating with product and engineering teams to translate business requirements into data solutions
Driving adoption of modern data practices across the organization
Contributing to architectural reviews and technical decision-making
Taking ownership of data problems through to resolution
JOB REQUIREMENT
A minimum of 8-10 years of experience in data engineering, demonstrating increasing level of responsibility for architecture and technical leadership
Recent 3-5 years of experience in tech startups, specializing in building and scaling data infrastructure in high-growth settings
Recent expertise in insurance, banking, fintech, or e-commerce, with a preference for insurance domain experience
Track record of building data platforms capable of handling production AI/ML workloads at scale
Profound knowledge of AWS and Azure cloud data services, with multi-cloud experience
Proficiency in MongoDB Atlas, including aggregation pipelines, Atlas Data Federation, and data modeling for document databases
Hands-on experience with pipeline orchestration tools such as Airflow, Spark (Databricks or Apache), and Kafka
Familiarity with transformation frameworks like dbt or similar
Advanced skills in SQL and Python
Experience with Infrastructure as Code (Terraform) and GitOps practices
Strong understanding of containerization (Docker, Kubernetes) for data workloads
Ability to build data pipelines for ML model training and inference
Hands-on experience with vector databases (e.g., MongoDB Atlas Vector Search, Pinecone, Weaviate, Qdrant)
Knowledge of MLOps practices including model versioning, feature stores, monitoring, and A/B testing infrastructure
Understanding of embedding models and retrieval-augmented generation (RAG) patterns
Active use of AI coding assistants (GitHub Copilot, Claude, Cursor, or similar) in daily work
Strong data modeling and architecture skills, with the ability to communicate designs to both technical and non-technical stakeholders
Self-directed and capable of managing and prioritizing autonomously
Collaborative mindset, comfortable working across engineering, product, and business teams
Comfortable with ambiguity, able to context-switch, and energized by building rather than maintaining, reflecting a startup mentality
WHAT'S ON OFFER
Competitive compensation package
Health benefits for yourself and two family members
Additional allowances for meals, phone, and transportation
Year-end bonus
Strong business and technical support
Positive and diverse work environment
Potential for travel and work in Southeast Asia
New, modern office in District 1 (HCM city)
Access to latest technologies and flexible work hours
Other perks and benefits available
CONTACT
We are
PEGASI – IT Recruitment Consultancy in Vietnam. If you are looking for new opportunity for your career path, kindly visit our website
www.pegasi.com.vn for your reference. Thank you!