Data Engineer
Tasks
- Analyze production issues and perform root cause analysis
- Define and implement data quality frameworks metrics and controls
- Develop and validate data pipelines with PySpark and Databricks
- Execute source to target reconciliation
- Identify investigate and resolve data anomalies
- Implement proactive monitoring and automated quality checks
- Monitor data across Databricks Lakehouse
- Optimize data processing jobs for performance and scalability
- Perform end to end data pipeline validation
- Run data quality checks and monitoring
- Track and manage data defects through resolution
- Validate business rules and transformation logic against source systems
- Write advanced SQL for data profiling and root cause analysis
Perks/Benefits
- N/A
Skills/Tech-stack
Amazon Web Services | Apache Airflow | Apache Kafka | Azure | CI/CD | Cloud platform | Data Lake | Data Monitoring | Data Observability | Data Testing | Data Warehouse | Databricks | Delta Lake | Dimensional Modeling | ELT | ETL | Google Cloud | Google Cloud Platform | HDFS | Hadoop | Hive | Lakehouse | PySpark | SQL | Snowflake Schema | Spark | Star Schema | Unity Catalog | Web Services
Education
N/A
Roles
Data Engineer | Data Quality | Data Quality Engineer | Engineer | Quality Engineer
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