Data Service Engineer

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