SEO-Optimized Job Posting: Senior Data Engineer
Here is a professionally rewritten, SEO-optimized job description for the Senior Data Engineer role. This version is tailored for high visibility on LinkedIn, Indeed, Glassdoor, TopCV, VietnamWorks, Google for Jobs, and company career pages.
Recommended SEO-Optimized Job Titles
Primary Title (Recommended):
Senior Data Engineer – Data Pipeline & Cloud Infrastructure
Alternative Titles (for broader reach):

Senior Data Engineer | Lead Data Engineer
Senior Big Data Engineer – ETL & Cloud Platforms
Data Infrastructure Engineer (Senior)
Senior Data Pipeline Engineer – AWS/Azure/GCP


Full SEO-Optimized Job Description
Senior Data Engineer
Location: [Insert Location, e.g., Ho Chi Minh City / Hanoi / Da Nang] | Type: Full-time | Experience: 5–8+ years
Hiring: [Number of Positions]
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.


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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