Azure DataOps Data Engineer – II
India - GurgaonFull-TimeMid-levelSoftware Engineering
Key Responsibilities
- Support production data platforms, ensuring high availability, reliability, and performance.
- Monitor data pipelines and jobs, proactively identifying and resolving failures, performance issues, and data discrepancies.
- Perform root cause analysis (RCA) for incidents and implement preventive measures.
- Implement DataOps best practices including automation, monitoring, alerting, and operational dashboards.
- Collaborate with cross-functional teams to support reporting, analytics, and downstream consumption.
- Maintain documentation for pipelines, operational runbooks, and support procedures.
- Participate in on-call and rotational shift support, including weekends or night shifts as required.
Required Skills & Qualifications
- 3–5 years of experience in Data Engineering / DataOps roles.
- Strong hands-on experience with:
- Azure Databricks (PySpark, Spark SQL, Delta Lake)
- Azure Data Factory (ADF) – pipelines, triggers, parameters, monitoring
- Azure Data Lake Storage (ADLS Gen2)
- Good understanding of ETL/ELT frameworks, batch and incremental processing.
- Strong SQL skills for data analysis and troubleshooting.
- Experience with production support, incident management, and SLA-driven environments.
- Familiarity with monitoring tools (Azure Monitor, Log Analytics, alerts).
- Understanding of Azure security concepts (RBAC, Managed Identity, Key Vault).
Good to Have
- Exposure to Microsoft Fabric (Lakehouse, Pipelines, Notebooks).
- Basic knowledge of Power BI and semantic models.
- Experience working in 24/7 support or rotational shift models.
Work Model
- Willingness to work in a rotational shift / on-call support model as part of a global operations team.
- Ability to handle high-priority incidents and work under time-sensitive conditions.
Key Traits
- Strong troubleshooting and analytical mindset.
- Ownership-driven and operationally focused.
- Clear communication and collaboration skills.
- Continuous learning attitude toward Azure data services and DataOps practices.
