Senior/ Lead Data Engineer

JOB DESCRIPTION

Architect, develop, and maintain scalable data infrastructure, including data lakes, pipelines, and metadata repositories, ensuring the timely and accurate delivery of data to stakeholders.
Work closely with data scientists to build and support data models, integrate data sources, and support machine learning workflows and experimentation environments.
Develop and optimize large-scale, batch, and real-time data processing systems to enhance operational efficiency and meet business objectives.
Leverage Python, Apache Airflow, and AWS services to automate data workflows and processes, ensuring efficient scheduling and monitoring.
Utilize AWS services such as S3, Glue, EC2, and Lambda to manage data storage and compute resources, ensuring high performance, scalability, and cost-efficiency.
Implement robust testing and validation procedures to ensure the reliability, accuracy, and security of data processing workflows.
Stay informed of industry best practices and emerging technologies in both data engineering and data science to propose optimizations and innovative solutions.

JOB REQUIREMENT

Core Expertise: Proficiency in Python for data processing and scripting (pandas, pyspark), workflow automation (Apache Airflow), and experience with AWS services (Glue, S3, EC2, Lambda).
Containerization & Orchestration: Experience working with Kubernetes and Docker for managing containerized environments in the cloud.
Data Engineering Tools: Hands-on experience with columnar and big data databases (Athena, Redshift, Vertica, Hive/Hadoop), along with version control systems like Git.
Cloud Services: Strong familiarity with AWS services for cloud-based data processing and management.
CI/CD Pipeline: Experience with CI/CD tools such as Jenkins, CircleCI, or AWS CodePipeline for continuous integration and deployment.
Data Engineering Focus (75%): Expertise in building and managing robust data architectures and pipelines for large-scale data operations.
Data Science Support (25%): Ability to support data science workflows, including collaboration on data preparation, feature engineering, and enabling experimentation environments.
Nice-to-have requirements:
Langchain Experience: Familiarity with Langchain for building data applications involving natural language processing or conversational AI frameworks.
Advanced Data Science Tools: Experience with AWS Sagemaker or Databricks for enabling machine learning environments.
Big Data & Analytics: Familiarity with both RDBMS (MySQL, PostgreSQL) and NoSQL (DynamoDB, Redis) databases.
BI Tools: Experience with enterprise BI tools like Tableau, Looker, or PowerBI.
Messaging & Event Streaming: Familiarity with distributed messaging systems like Kafka or RabbitMQ for event streaming.
Monitoring & Logging: Experience with monitoring and log management tools such as the ELK stack or Datadog.
Data Privacy and Security: Knowledge of best practices for ensuring data privacy and security, particularly in large data infrastructures.

WHAT'S ON OFFER

Competitive salary
13th-month salary guarantee
Performance bonus
Professional English course for employees
Premium health insurance
Extensive annual leave

CONTACT

PEGASI – IT Recruitment Consultancy | Email: recruit@pegasi.com.vn | Tel: +84 28 3622 8666
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!

Job Summary

Company Type:

Outsource

Technical Skills:

Data Engineering

Location:

Ho Chi Minh, Ha Noi - Viet Nam

Working Policy:

Hybrid

Job ID:

J01942

Status:

Active

Related Job:

Software Engineer (Node.js) - Database

Ho Chi Minh - Viet Nam


Product

  • NodeJS

Design system architectures, establish coding standards, and construct cohesive, cloud-native solutions. Develop high-quality Node.js code, optimize system performance, and tackle complex software integration challenges. Oversee the testing, deployment, and comprehensive documentation of integrated systems. Mentor less-experienced engineers, engage in cross-functional teamwork, and ensure solutions meet business requirements and international standards. Participate actively in all Agile software development phases, including creating user stories and executing sprint planning Engage with multinational companies, demonstrating flexibility to occasionally adapt to US and EU time zones.

Negotiation

View details

Software Engineer (Node.js) - Platform Security

Ho Chi Minh - Viet Nam


Product

  • NodeJS

Design system architectures, establish coding standards, and construct cohesive, cloud-native solutions. Develop high-quality Node.js code, strengthen system security and reliability, and tackle complex software integration challenges. Design and implement platform security controls across web applications, APIs, and cloud services, including authentication, authorization, session management, secrets management, encryption, and audit logging. Identify and remediate security risks through threat modeling, secure code reviews, automated security testing, dependency scanning, and investigation of security-related issues. Oversee the testing, deployment, and comprehensive documentation of integrated systems. Mentor less-experienced engineers, engage in cross-functional teamwork, and ensure solutions meet business requirements and international standards. Participate actively in all Agile software development phases, including creating user stories and executing sprint planning Engage with multinational companies, demonstrating flexibility to occasionally adapt to US and EU time zones.

Negotiation

View details

AI Agent Ops Engineer

Ho Chi Minh - Viet Nam


Product

  • AI

#Agent Engineering & operation Design, build, and maintain production-grade AI agent systems, including: context engineering and instruction architecture, prompt hardening and safe execution boundaries, tool integrations and multi-step orchestration, memory strategies and reliability patterns. Own the full agent lifecycle: prototype → evaluate → deploy → monitor → iterate. Build and maintain an evaluation pipeline to measure agent quality, catch regressions, and enforce deployment gates (golden datasets, scenario suites, automated checks). Instrument agents and agent platforms for production observability: structured logging, tracing, and metrics; latency and cost monitoring; tool-call success rates and failure analysis. Define operational readiness standards including: rollback criteria, incident response playbooks, recovery paths for common failure modes.#Team Enablement & Coaching Embed with product engineering teams to identify high-value use cases ready for agent automation. We will be operating in a Central Agent Ops role enabling Ai product builders through AI enablers. Translate business workflows into agent-executable tasks with clear: contact boundaries/interfaces, assumptions and inputs/outputs, failure modes and safe fallbacks. Deliver targeted coaching to engineers on: context engineering best practices, harness design and regression testing patterns, agent skill design and tool-contract discipline. Reduce onboarding time for teams adopting AI capabilities-from first conversation to a production-ready agent. Train product engineers to extend and maintain agent skills independently.#Standards & Knowledge operations Author and maintain org-level standards for agents, including: naming conventions, context file structures and ownership rules, skill interface contracts (inputs/outputs, invariants, error handling), evaluation criteria and release quality bars. Establish and enforce "repo-as-discipline" practices so agent knowledge is: versioned, reviewable, discoverable, reusable; not trapped in prompt snippets or individual heads. Build and grow a shared agent skills library that teams can reuse and extend. Track and aggregate AI tooling/framework updates and external best practices, serving as a central intake so product teams don't each have to follow the entire AI landscape. Run internal knowledge-sharing sessions, showcases, and retrospectives to propagate learnings efficiently.

Negotiation

View details