Senior/ Lead Data Engineer (Data Platform / MLOps)

You will be responsible for managing, designing, and enhancing data systems and workflows that drive key business decisions. The role is focused 75% on data engineering, involving the construction and optimization of data pipelines and architectures, and 25% on supporting data science initiatives through collaboration with data science teams for machine learning workflows and advanced analytics. You will leverage technologies like Python, Airflow, Kubernetes, and AWS to deliver high-quality data solutions.

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.

7-8+ years of dedicated experience as a Data Engineer.

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.

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.

Competitive salary

13th-month salary guarantee

Performance bonus

Professional English course for employees

Premium health insurance

Extensive annual leave

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