Principal Java Data Engineer
Tasks
- Automate data freshness completeness and accuracy checks
- Conduct peer reviews and provide feedback
- Define SLAs and SLOs for latency and throughput
- Define schema contracts and transformation tests for data quality
- Develop batch and real time data solutions
- Drive modernization with event driven and cloud native architectures
- Enforce backward compatible schema evolution
- Engineer and optimize data pipelines
- Implement data governance and observability best practices
- Implement metrics logging distributed tracing for streaming jobs
- Integrate alerting and dashboards for proactive monitoring
- Lead design and implementation of scalable distributed systems with Java microservices
- Mentor data engineering team members
Perks/Benefits
- Allyship and inclusion communities
- Caregiver leave
- Continuous development support program
- Employee assistance program
- Employee recognition
- Fertility and adoption support
- Flexible paid time off
- Parental leave
- Retirement plan matching
- Wellness support programs
Skills/Tech-stack
ADLS Gen2 | AWS | Alerting | Apache Hudi | Apache Spark | Apache Trino | Azure | Azure ADLS | Azure ADLS Gen2 | Azure Data | Azure Data Lake | Azure Data Lake Storage | Batch Processing | CI/CD | Containerization | DBT | Data Governance | Data Lake Storage | Data Quality | Databricks | Distributed Systems | Distributed tracing | Docker | Event Driven | Event-driven architecture | GCP | Great Expectations | HDFS | Java | Kubernetes | Lakehouse | Logging | Metrics | Microservices | Observability | Performance Tuning | Schema Contracts | Schema evolution | Streaming | Trino
Education
N/A
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