Data Engineering Operations Lead
Tasks
- Assign tasks and track progress
- Collaborate with data scientists and analysts
- Coordinate downstream consumers
- Coordinate upstream dependencies
- Define operational standards
- Design, build, test and deploy data solutions
- Drive continuous improvement pipeline reliability observability efficiency
- Drive root cause analysis
- Implement data governance best practices
- Improve data quality monitoring
- Lead incident management practices
- Lead standups and sprint planning
- Maintain pipeline health
- Manage runbooks and alerting thresholds
- Mentor junior engineers
- Monitor data ingestion pipelines
- Onboard new data sources
- Organize daily team workload
- Own data engineering operations
- Plan and delegate technical delivery
- Resolve pipeline incidents
- Triage pipeline failures
Perks/Benefits
- Contributory pension
- Diversity forums
- Hybrid work
- Mindfulness app access
- Networking groups
- Paid time off
- Private health care
- Remote work flexibility
- Wellbeing programs
- Work Life Flexibility Programs
Skills/Tech-stack
Apache Airflow | Apache Hive | Apache Spark | Batch Processing | CI/CD | Cloud Composer | Cloud Dataflow | Cloud Dataproc | Cloud Functions | Cloud PubSub | Cloud Storage | Data Governance | Data Mart | Data Mart Design | Data Modeling | Data Pipelines | Data Quality | Data Warehouse | Data quality monitoring | Delta Lake | ETL | Git | Google BigQuery | Google Cloud | Google Cloud Dataflow | Google Cloud Dataproc | Google Cloud Functions | Google Cloud PubSub | Google Cloud Storage | Incident Management | Lakehouse | Observability | On-Call | Pandas | Performance optimization | PySpark | Python | Quality monitoring | Real Time | Real-time Streaming | RunBook | SLA management | SQL | SQL Server | SSMS | SparkSQL | TSQL
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