Data Service Engineer

Siam Makro Public Company Limited

. Monitor scheduled and event-driven ETL/ELT pipelines across Azure Data Factory, Databricks, Airflow, and related platforms .

Posted 9/24/2026full-timeBangkok • ThailandJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in monitoring and supporting ETL/ELT pipelines using Azure Data Factory, Databricks, and Airflow, with strong SQL and Python skills. Capable of performing root-cause analysis, automating operational tasks, and ensuring data quality and integrity across data platforms.

Highest-signal resume keywords

Azure Data FactorySQLData EngineeringETL/ELTPython

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data OperationsProduction SupportData Quality ControlsTroubleshootingData WarehousingJob DependenciesShell ScriptingCI/CD PracticesMonitoring ToolsAccess Controls

Soft Skills

Clear CommunicationMethodical TroubleshootingCollaboration

Tools & Technologies

DatabricksApache SparkAirflowAzure MonitorCloudWatchGrafanaJira

Industry Keywords

RetailE-commerceFinanceSupply-ChainEnterprise Analytics

Tech Stack

Tools & technologies

AirflowApacheAWSAzureETLGrafanaLinuxPythonShell ScriptingSparkSQL

About the role

Key responsibilities & impact

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
Respond to data-service incidents and operational requests within agreed service levels
Perform root-cause analysis and document issues, impacts, resolutions, and preventive actions
Create, update, and follow operational tickets through closure
Coordinate with source-system owners, data engineers, infrastructure teams, and report owners
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
Support users of reports, dashboards, and downstream data products
Provide updates on incidents, blockers, ownership, and next actions
Participate in daily operational reviews and handovers
Maintain runbooks, troubleshooting guides, support knowledge, and service documentation
Automate repetitive operational tasks and recovery steps
Contribute to observability, cost, performance, and reliability improvements
Support standardization of deployment, support, and data-quality practices
Share lessons learned and improve team operational readiness

Requirements

What you’ll need

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 Azure Data Factory, Databricks, Apache Spark, Airflow, or similar orchestration or processing technologies
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 Azure Monitor, CloudWatch, Grafana, or equivalent monitoring 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