Net2Source is a Global Workforce Solutions Company headquartered at NJ, USA, and they are seeking a DataOps Engineer. The role involves monitoring and maintaining data pipelines, performing SQL analysis, and collaborating with various teams to resolve data-related incidents.
Responsibilities:
- Monitor, support, and maintain enterprise data pipelines and ETL workflows
- Perform advanced SQL analysis for troubleshooting, reconciliation, and operational reporting Investigate and resolve production data issues, failures, latency, and data discrepancies Conduct root cause analysis across databases, ETL jobs, APIs, and downstream systems Support daily operational health checks, incident management, and SLA adherence
- Analyze large datasets to identify anomalies, trends, and data quality issues
- Collaborate with business users, application teams, and platform engineering teams to resolve data-related incidents
- Execute data validation, reconciliation, and integrity checks across multiple systems
- Optimize SQL queries and data processing performance
- Support batch and near real-time data movement processes
- Build and maintain operational dashboards, alerts, and monitoring solutions
- Assist with deployment validation, release support, and production readiness activities
- Create operational runbooks, troubleshooting guides, and support documentation
- Drive automation initiatives for monitoring, alerting, and issue remediation
Requirements:
- Strong SQL expertise including: Complex joins and subqueries, Query tuning and optimization, Window functions, Stored procedures, Data analysis and reconciliation
- Hands-on experience supporting ETL/ELT processes and workflows
- Experience with ETL tools/platforms such as: Azure Data Factory, SSIS
- Strong analytical and troubleshooting skills in production support environments
- Experience investigating data quality, transformation, and integration issues
- Knowledge of relational and cloud databases: SQL Server, Azure SQL
- Familiarity with monitoring and observability tools
- Understanding of data lifecycle management and operational support processes