Data Service Engineer

About this role

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.

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