Today
Optimized the data ingestion pipeline, reducing runtime by ~30% across 4 Airflow tasks. Profiled the bottleneck and refactored the chunked S3 reads.
Prototyped a new anomaly detection workflow using isolation forests; early tests show 25% reduction in false positives compared to z-score method.
Debugged a schema drift causing silent failures in downstream analytics job. Root cause was a partner API changing date format without notification.
This Week
Aligned with the product team on success metrics for the LLM integration. Defined clear KPIs: response accuracy, latency p95, and user satisfaction scores.
Implemented incremental refresh logic for the customer segmentation model. Now only processes changed records, cutting compute costs by 60%.
Refactored the feature engineering pipeline to use dbt macros. This will make it easier for analysts to contribute transformations without Python knowledge.
Presented pipeline reliability improvements to leadership. Showed 40% reduction in data incidents and faster MTTR. Team appreciated the visualizations.
Investigated memory leak in the real-time aggregation service. Traced to unclosed Kafka consumers; implemented proper cleanup and resource pooling.
Last Week
Shipped the new customer churn prediction model to production. Early validation shows 15% improvement in precision over the baseline model.
Added comprehensive data quality checks to the warehouse sync job. Now catching schema mismatches and null violations before they propagate downstream.
Collaborated with the ML platform team to migrate embedding generation to GPU instances. Reduced batch processing time from 4 hours to 25 minutes.
Dealt with data corruption in historical partitions. Had to backfill 2 months of data—took coordination with infra team and careful validation.
Built an internal dashboard for monitoring pipeline health metrics. Engineering and product teams now have real-time visibility into data freshness and quality.
Last 30 Days
Led the quarterly data platform roadmap planning session. Prioritized scalability improvements and new self-service features for analysts.
Implemented feature flags for the new recommendation engine. Allows us to gradually roll out to users and measure impact safely.
Open-sourced our internal data validation library. Got positive feedback from the community and 50+ stars on GitHub in the first week.
Navigated a complex stakeholder discussion about data retention policies. Balanced compliance requirements with analytical needs and storage costs.