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

• 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.

• 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.

• 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.

• 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.

• 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.

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