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