Data Engineer

Data Engineer (Mid/Senior)

Level:

2–4 years experience \|

Location:

Hanoi (Hoa Lac) \|

Language:

English (Business Proficiency) \|

Type:

Full-time

Job Description

1\. Data Pipeline \& Integration

• Design, build, and maintain scalable ETL/ELT pipelines (batch \& streaming)
• Integrate data from diverse sources: databases, APIs, event streams, flat files
• Develop ingestion workflows using Airflow, Spark, Kafka
• Ensure data quality, consistency, and completeness

2\. Data Modeling \& Warehouse

• Design dimensional data models (star/snowflake schema)
• Build and maintain data warehouse/lakehouse on cloud (Redshift, Synapse, BigQuery, Databricks)
• Define data standards: naming conventions, data dictionaries, lineage
• Optimize query performance (partitioning, clustering, indexing, materialized views)

3\. Data Infrastructure \& MLOps Support

• Manage data infrastructure using IaC (Terraform, CloudFormation)
• Build feature stores and pipelines feeding AI/ML models
• Implement data observability: SLA tracking, anomaly detection, drift alerts
• Support CI/CD for data pipelines

4\. Collaboration \& Communication

• Work with AI Engineers, Solution Architects, and Business Analysts to define data requirements
• Communicate architecture decisions clearly in English
• Participate in Agile/Scrum ceremonies (planning, stand-up, demo, retro)
• Maintain documentation: data catalogs, runbooks, data contracts

Qualifications

Must-Have:

• 2–3\+ years of hands-on data engineering experience in production environments
• Proficiency in SQL (complex queries, window functions, performance tuning) and Python (pandas, PySpark)
• Experience with at least one orchestration framework (Airflow, Prefect, Dagster)
• Hands-on experience with a cloud data warehouse/lakehouse (BigQuery, Redshift, Synapse, Databricks)
• Understanding of data modeling: normalization, dimensional modeling, SCD
• English proficiency — able to run technical meetings and write documentation
• Understanding of SDLC: Git workflow, code review, CI/CD, Agile/Scrum

Nice-to-Have:

• Experience with streaming platforms: Kafka, Kinesis, Event Hubs
• Familiarity with dbt for transformation layer
• Knowledge of lakehouse architecture: Delta Lake, Iceberg, Hudi
• MLOps exposure: feature pipelines, training dataset management
• Experience in retail/e-commerce/SaaS domain (transaction, loyalty, inventory)
• Cloud certifications: AWS Data Analytics, Azure Data Engineer, or GCP Professional Data Engineer

Benefits

• Competitive salary \+ Project Bonuses based on KPIs \& Client Satisfaction Index
• Professional growth with global client exposure; clear path to Delivery Manager/Operations Manager
• Premium health insurance, annual performance bonuses, certification sponsorship