Data Service Engineer

Location: Bangkok, Bangkok, Thailand

Department: Technology

Workplace: hybrid

Description

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.

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.

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:

· 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