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