Job overview
We are looking for a Data Engineer who can deliver in a Thai white-collar team. Companies with growing data volume (e-commerce, fintech, logistics) that need reliable pipelines feeding dashboards and models. Build the pipelines that turn raw data into something usable. Use this page as a ready job description: overview, responsibilities, salary guidance for Thailand, qualifications, and a template you can paste into a free job post.
Job responsibilities for Data Engineer
As a Data Engineer, day-to-day work typically includes the items below. Edit the list so it matches your stack, tools, and team size before you publish.
- Build and maintain ETL/ELT pipelines from multiple data sources
- Design and optimize data warehouse schemas (e.g. BigQuery, Snowflake, Redshift)
- Orchestrate workflows with tools like Airflow or dbt
- Ensure data quality, monitoring, and lineage tracking
- Work with analysts and data scientists to expose clean, reliable datasets
Data Engineer salaries in Thailand
- Typical posted band: ฿35,000-65,000
- Use the WorkVenture salary estimator for Data Engineer bands in Thailand.
- Exact pay depends on seniority, English need, industry, and Bangkok vs upcountry.
Data Engineer job qualifications
Basic qualifications to consider for a Data Engineer in Thailand include:
- 2+ years building data pipelines in production
- Strong SQL and at least one scripting language (Python preferred)
- Experience with a cloud data warehouse and an orchestration tool
- Understands data modeling for analytics, not just transactional systems
Data Engineer skills required
Beyond formal qualifications, strong Data Engineer hires usually bring a mix of tools and working style:
- Experience with Spark or another distributed processing framework
- Familiarity with data governance or PII handling requirements
Candidates also care about how you frame the role:
- Name the actual warehouse and orchestration tool in use, this role has enough tool fragmentation that a vague ad wastes both sides' time
- Mention data volume or source count, a data engineer sizes the job very differently for 5 sources versus 50
What to expect as a Data Engineer
What the role often feels like in practice (use this to set expectations in interviews):
- Build and maintain ETL/ELT pipelines from multiple data sources
- Design and optimize data warehouse schemas (e.g. BigQuery, Snowflake, Redshift)
- Orchestrate workflows with tools like Airflow or dbt
- Ensure data quality, monitoring, and lineage tracking
Job description template
Copy the block below into your ATS or free job post. Replace company name, tools, and location before publishing.
Data Engineer
About us: Companies with growing data volume (e-commerce, fintech, logistics) that need reliable pipelines feeding dashboards and models.
Responsibilities:
- Build and maintain ETL/ELT pipelines from multiple data sources
- Design and optimize data warehouse schemas (e.g. BigQuery, Snowflake, Redshift)
- Orchestrate workflows with tools like Airflow or dbt
- Ensure data quality, monitoring, and lineage tracking
- Work with analysts and data scientists to expose clean, reliable datasets
Requirements:
- 2+ years building data pipelines in production
- Strong SQL and at least one scripting language (Python preferred)
- Experience with a cloud data warehouse and an orchestration tool
- Understands data modeling for analytics, not just transactional systems
Nice to have:
- Experience with Spark or another distributed processing framework
- Familiarity with data governance or PII handling requirements
Compensation: ฿35,000-65,000
Benefits: Social security, group health (as applicable), annual leave per company policy
Location: Bangkok office / hybrid (edit to match)
How to apply: Apply via this job post or email your CV to [email protected]
Job title: Data Engineer About us: Companies with growing data volume (e-commerce, fintech, logistics) that need reliable pipelines feeding dashboards and models. Responsibilities: - Build and maintain ETL/ELT pipelines from multiple data sources - Design and optimize data warehouse schemas (e.g. BigQuery, Snowflake, Redshift) - Orchestrate workflows with tools like Airflow or dbt - Ensure data quality, monitoring, and lineage tracking - Work with analysts and data scientists to expose clean, reliable datasets Requirements: - 2+ years building data pipelines in production - Strong SQL and at least one scripting language (Python preferred) - Experience with a cloud data warehouse and an orchestration tool - Understands data modeling for analytics, not just transactional systems Nice to have: - Experience with Spark or another distributed processing framework - Familiarity with data governance or PII handling requirements Compensation: ฿35,000-65,000 Benefits: Social security, group health (as applicable), annual leave per company policy Location: Bangkok office / hybrid (edit to match) How to apply: Apply via this job post or email your CV to [email protected]
Tips for writing this job ad
- Name the actual warehouse and orchestration tool in use, this role has enough tool fragmentation that a vague ad wastes both sides' time
- Mention data volume or source count, a data engineer sizes the job very differently for 5 sources versus 50
Weak ad: Vague title. Competitive salary. Hardworking.
Strong ad: Data Engineer. ฿35,000-65,000. Companies with growing data volume (e-commerce, fintech, logistics) that need reliable pipelines feeding dashboards and models.
FAQ
Data engineer vs data analyst, what is the difference?
A data analyst answers business questions using existing data. A data engineer builds and maintains the pipelines and infrastructure that get that data into a usable state in the first place. If your data is messy and scattered across systems, you need a data engineer before an analyst can do much with it.
What should I look for beyond technical tools?
Ask them to describe a time a pipeline silently produced wrong numbers and how they caught it. Good data engineers think about data quality checks by default, not just moving data from A to B.
How do I write a stronger job ad for this role?
Describe what the data is actually used for downstream, like feeding a recommendation model or a finance dashboard. Engineers care about the impact of the pipelines they build, not just the tool list.
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