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