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Chaithanya Konda
Mid-level Data Engineer specializing in multi-cloud analytics platforms
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Data engineer with hands-on GCP platform experience spanning BigQuery, Cloud SQL, Dataflow, and Cloud Composer, including both production operations and cloud migration work. They led a migration from legacy SQL Server/Oracle systems to a cloud-native BigQuery architecture and cite measurable impact: processing reduced from hours to minutes, query latency improved 60%+, and ingestion time improved 40%.
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Key Strengths
- Led migration from on-prem SQL Server/Oracle to GCP BigQuery data warehouse
- Strong GCP data platform operations across BigQuery and Cloud SQL
- Performance tuning expertise in partitioning, clustering, and SQL optimization
- Built reusable zero-touch automation with Terraform and Python
- Improved query latency by over 60%
- Reduced ingestion time by 40%
- Cut data processing time from hours to minutes
- Designed reliable data pipelines with Dataflow, Apache Beam, and Cloud Composer
- Implemented HA/DR, replication, and backup strategies
- Applied RBAC and audit logging to meet data governance requirements
- Resolved high-severity analytics SLA issues through bottleneck isolation and pipeline redesign
- Cross-functional collaboration with analysts, scientists, and migration stakeholders
- Owned end-to-end healthcare/operational data pipelines (bronze→silver→gold) from ingestion through serving
- Built automated data validation at scale using Great Expectations plus custom Python checks
- Handled schema evolution using Delta Lake schema enforcement and time travel rollback
- Optimized large-volume Spark pipelines (GBs to TBs/day) using broadcast joins and execution optimizations to improve speed and reduce compute cost
- Implemented proactive monitoring/alerting with Azure Monitor and Log Analytics to support SLA compliance and faster issue resolution
- Designed external data collection with retries (exponential backoff), anti-bot mitigations, schema validation, and parameterized backfills
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