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Check with seller SENIOR DATA ENGINEER
- Location: Ha Noi City, vietnam
We are seeking an experienced Senior Data Engineer to join our data platform team. In this senior role, you will design, build, optimize, and maintain scalable data pipelines and infrastructure that power analytics, machine learning, and business intelligence initiatives. You will work with large-scale datasets, modern cloud technologies, and real-time processing systems to ensure data is reliable, accessible, and delivered efficiently. If you are a strong problem-solver with deep expertise in data engineering, cloud platforms, and big data technologies, this is a high-impact opportunity to drive data-driven transformation.
Key Responsibilities
Data Pipeline Development: Design, develop, and maintain robust ETL/ELT pipelines for batch and real-time data processing using tools like Apache Spark, Airflow, Kafka, and Flink.
Cloud Data Architecture: Architect and manage data platforms on cloud services (AWS, Azure, GCP) including data lakes, data warehouses (Snowflake, BigQuery, Redshift), and lakehouse architectures.
Data Integration & Quality: Build scalable data ingestion processes from diverse sources (databases, APIs, streaming, SaaS platforms); implement data validation, cleansing, and quality monitoring.
Performance Optimization: Optimize data processing jobs for cost, speed, and reliability; troubleshoot performance bottlenecks and implement monitoring/alerting systems.
Collaboration with Stakeholders: Work closely with Data Scientists, Analysts, Software Engineers, and business teams to understand requirements and deliver reliable data solutions.
Infrastructure as Code & Automation: Implement CI/CD pipelines, Infrastructure as Code (Terraform, CloudFormation), and automated testing for data workflows.
Governance & Security: Ensure data security, compliance (GDPR, Vietnam data protection laws), lineage tracking, and access control policies.
Mentorship & Leadership: Mentor junior data engineers, establish best practices, and contribute to technical roadmaps for the data platform.
Requirements & Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a related quantitative field.
Experience: 5–8+ years of progressive experience as a Data Engineer, with at least 2–3 years in a senior or lead capacity. Strong portfolio or proven track record with large-scale data systems is required.
Technical Skills:
Advanced proficiency in Python (or Scala/Java), SQL, and scripting for data processing.
Deep experience with big data frameworks: Apache Spark, Hadoop, Kafka, Airflow.
Strong hands-on experience with cloud platforms (AWS Glue, EMR, Azure Data Factory, GCP Dataflow) and modern data tools (dbt, Snowflake, Databricks).
Solid understanding of data modeling, warehousing concepts, and streaming architectures.
Experience with containerization (Docker, Kubernetes) and version control (Git).
Knowledge of data governance, security, and observability tools.
Soft Skills:
Excellent problem-solving, analytical thinking, and debugging abilities.
Strong communication and collaboration skills across technical and non-technical teams.
Leadership mindset with the ability to mentor and drive technical decisions.
Proactive, results-oriented, and comfortable in fast-paced, agile environments.
What We Offer
Competitive salary package with attractive performance bonuses and technical allowances.
Comprehensive benefits: social insurance (BHXH), health insurance (BHYT), unemployment insurance, and annual health check-ups.
Professional development opportunities, including certifications (AWS Certified Data Engineer, Azure Data Engineer, Databricks, Snowflake), conferences, and latest tools training.
Career growth into Lead Data Engineer, Data Architect, or Head of Data Engineering roles.
Annual leave, team-building activities, flexible/hybrid working options, and a dynamic, innovation-driven work culture.
Tags: Senior Data Engineer, Data Pipeline Engineer, Big Data Engineer, Cloud Data Engineer, ETL Developer, Data Infrastructure, AWS Data Engineer, Azure Data Factory, Spark Data Engineer, Data Warehouse Engineer, dbt, Snowflake, Databricks.
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