Nezda Technologies, Inc.
Senior Data Engineer
Data Operations Senior Engineer who runs and improves production data workloads supporting SLHK on AWS: managing scheduled pipelines (Control-M/Airflow), troubleshooting ETL with SQL, Python and Spark/PySpark, handling incidents and SLA monitoring, executing releases, and automating data quality and operational reporting.
- Compensation
- Not disclosed
- Location
- Taguig, Philippines
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 8+ years of overall IT/data engineering experience
- 3–6 years hands-on experience in DataOps, ETL, data engineering, or production data operations
- Strong experience with AWS cloud services and cloud-based data environments
- Extensive hands-on SQL for data analysis, troubleshooting, validation, and reconciliation
- Strong Python programming for automation, data processing, and operational tooling
- Hands-on experience with Apache Spark / PySpark for large-scale data processing
- Experience with enterprise scheduling/orchestration tools (Control-M, AutoSys, Tidal, Stonebranch, Redwood, ActiveBatch, or Airflow)
- Experience in production support, incident management, change/release execution, and SLA monitoring
Nice to have
- Exposure to Data Quality, Data Lineage, Metadata Management, and Data Governance processes
Full description
The Data Operations Senior Engineer is responsible for operating, monitoring, and continuously improving production data workloads supporting SLHK. The role focuses on DataOps, ETL operations, cloud data platforms, automation, and production support, ensuring data pipelines meet operational, quality, and SLA requirements.
Key Qualifications
- At least 8+ years of overall IT/data engineering experience.
- 3–6 years of hands-on experience in DataOps, ETL, data engineering, or production data operations.
- Strong experience with AWS cloud services and cloud-based data environments.
- Extensive hands-on experience with SQL for data analysis, troubleshooting, validation, and reconciliation.
- Strong programming experience in Python for automation, data processing, and operational tooling.
- Hands-on experience with Apache Spark / PySpark for large-scale data processing.
- Experience with enterprise scheduling and orchestration tools such as Control-M, AutoSys, Tidal, Stonebranch, Redwood, ActiveBatch, or Airflow.
- Experience in production support, incident management, change/release execution, and SLA monitoring.
- Exposure to Data Quality, Data Lineage, Metadata Management, and Data Governance processes is a plus.
Key Responsibilities
Scheduling & Production Monitoring
- Own and manage daily/weekly production schedules using enterprise scheduling tools such as Control-M/Airflow.
- Maintain calendars, job dependencies, alerts, and operational health checks.
- Monitor AWS-based data workloads and ensure timely completion of critical data pipelines.
Data Operations & ETL Support
- Operate and troubleshoot production ETL/data pipelines across cloud and enterprise environments.
- Use SQL, Python, and Spark/PySpark to investigate data issues, perform validations, and support data processing.
- Identify opportunities to improve pipeline reliability, performance, and operational efficiency.
Incident & Problem Management
- Perform first-line triage and troubleshooting of production data incidents.
- Analyze job failures, data discrepancies, performance issues, and SLA breaches.
- Document RCA, corrective actions, and preventive measures while maintaining runbooks and knowledge bases.
Change & Release Execution
- Execute production deployments, configuration changes, and data pipeline updates following established change controls.
- Prepare implementation steps, validation procedures, rollback plans, and complete deployment evidence.