Data Service Engineer
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Before you apply
LocationBangkok, Bangkok, Thailand
Typefull-time
SeniorityMid
Experience2–5 yrs
DepartmentTechnology
Company size10,000+ people
PostedSep 24, 2026 · 1mo ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related discipline, or equivalent practical experience
2–5 years of experience in data engineering, data operations, application support, or production support
Hands-on experience supporting production data pipelines or data platforms
Strong SQL skills
Working knowledge of Python or another scripting language
Experience with one or more orchestration or processing technologies such as Azure Data Factory, Databricks, Apache Spark, or Airflow
Understanding of data warehousing, ETL/ELT, file and database integration, job dependencies, and data-quality controls
Ability to troubleshoot methodically, communicate clearly, and work across technical and business teams
Experience with Azure or AWS data services
Experience with Linux, shell scripting, Git, and CI/CD practices
Familiarity with monitoring platforms such as Azure Monitor, CloudWatch, Grafana, or equivalent tools
Experience with Jira or an IT service-management platform
Retail, e-commerce, finance, supply-chain, or enterprise analytics experience
Knowledge of access controls, secrets management, and secure production-support practices
Skills
SQL
Python
Azure Data Factory
Databricks
Apache Spark
Airflow
Azure
AWS
Linux
Git
CI/CD
Azure Monitor
CloudWatch
Grafana
Jira
About the role
The Data Service Engineer supports the day-to-day reliability of enterprise data services. The role monitors data pipelines, investigates production failures, resolves data-quality and integration issues, coordinates incident follow-up, and helps ensure that trusted data is available to reporting, analytics, and downstream business processes.
Responsibilities
Key Responsibilities
1.
Data Pipeline Operations
· Monitor scheduled and event-driven ETL/ELT pipelines across Azure Data Factory, Databricks, Airflow, and related platforms.
· Investigate failed jobs, delayed data, missing records, schema changes, and dependency issues.
· Rerun or recover pipelines using approved operational procedures and confirm successful completion.
· Support production releases, cutovers, and post-deployment monitoring.
2.
Incident and Problem Management
· Respond to data-service incidents and operational requests within agreed service levels.
· Perform root-cause analysis and document the issue, impact, resolution, and preventive action.
· Create, update, and follow operational tickets through closure.
· Coordinate with source-system owners, data engineers, infrastructure teams, and report owners when cross-team support is required.
3.
Data Quality and Reliability
· Validate data completeness, accuracy, freshness, and reconciliation results.
· Maintain monitoring, alerting, and operational checks for critical pipelines and datasets.
· Identify recurring failure patterns and recommend permanent fixes or automation.
· Escalate material data risks with clear impact and status communication.
4.
Stakeholder and Service Support
· Support users of reports, dashboards, and downstream data products.
· Provide concise updates on incidents, blockers, ownership, and expected next actions.
· Participate in daily operational reviews and handovers.
· Maintain runbooks, troubleshooting guides, support knowledge, and service documentation.
5.
Continuous Improvement
· Automate repetitive operational tasks and recovery steps where appropriate.
· Contribute to observability, cost, performance, and reliability improvements.
· Support standardization of deployment, support, and data-quality practices.
· Share lessons learned and help improve team operational readiness.
Requirements
Required Qualifications
· Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related discipline, or equivalent practical experience.
· 2–5 years of experience in data engineering, data operations, application support, or production support.
· Hands-on experience supporting production data pipelines or data platforms.
· Strong SQL skills and working knowledge of Python or another scripting language.
· Experience with one or more orchestration or processing technologies such as Azure Data Factory, Databricks, Apache Spark, or Airflow.
· Understanding of data warehousing, ETL/ELT, file and database integration, job dependencies, and data-quality controls.
· Ability to troubleshoot methodically, communicate clearly, and work across technical and business teams.
Requirements
Preferred qualifications:
Nice to have
· Experience with Azure or AWS data services.
· Experience with Linux, shell scripting, Git, and CI/CD practices.
· Familiarity with monitoring platforms such as Azure Monitor, CloudWatch, Grafana, or equivalent tools.
· Experience with Jira or an IT service-management platform.
· Retail, e-commerce, finance, supply-chain, or enterprise analytics experience.
· Knowledge of access controls, secrets management, and secure production-support practices.
Key competencies:
· Production ownership and service mindset
· Structured troubleshooting and root-cause analysis
· Attention to data quality and operational detail
· Clear incident communication and stakeholder coordination
· Prioritization under pressure
· Continuous improvement and automation mindset