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