I specialize in architecting fault-tolerant, distributed streaming systems and highly scalable real-time data pipelines[cite: 5]. My engineering approach focuses on enforcing exactly-once processing semantics, deep framework optimization, and strict infrastructure-as-code paradigms to process 100M+ events/day securely and reliably[cite: 5].

• PR: github.com/apache/airflow/pull/68298[cite: 5]
• Impact: Engineered the native deferrable async mode for the core SFTPOperator utilizing Python and Airflow’s Triggerer architecture[cite: 5]. This structural change freed up blocked worker slots by up to 90% during long-running I/O file transfers, eliminating massive distributed queue bottlenecks in production pipelines[cite: 5].
• Optimization: Redesigned SFTPHookAsync using low-level async I/O, implementing strict bounded concurrency via asyncio.Semaphore and asyncio.gather to perfectly optimize high-throughput parallel transfers[cite: 5].
• Type-Safety: Refactored execution engine logic to enforce a type-safe str, Enum architecture (SFTPOperation), entirely removing redundant code pathways across distributed GET, PUT, and DELETE mutations[cite: 5].

Stack: Python, SQL, Apache Kafka, PySpark, Spark Structured Streaming, Airflow, PostgreSQL, AWS S3, Docker[cite: 5]

• Designed an end-to-end Kafka-PySpark event-driven streaming architecture ingest-to-sink pipeline processing 100K–500K daily financial transactions at 5K events/sec[cite: 5].
• Maintained <500ms latency (48x performance boost over traditional batch methods) with 99.7% systemic uptime while capturing 98% of fraudulent patterns[cite: 5].
• Developed stateful stream window aggregations integrated with ML features, successfully slicing false-positive operational overhead from 8% down to <2%[cite: 5].

Stack: Python, SQL, Apache Airflow, AWS Glue, AWS S3, Redshift, Athena, PostgreSQL, Pandas, Docker[cite: 5]

• Built an enterprise multi-source AWS Glue ETL engine parsing 250K records/month into an automated Medallion Architecture (Bronze $\rightarrow$ Silver $\rightarrow$ Gold)[cite: 5].
• Implemented highly efficient log-based Change Data Capture (CDC) pipelines, reducing raw computing time by 60% (slashing runtime execution from 8 hours down to 3.2 hours)[cite: 5].
• Optimized high-velocity SQL transformations and vectorised Pandas structures to lower raw-to-analytical data latency by 60% while serving 120+ active business users[cite: 5].

Stack: Python, SQL, Airflow, AWS Glue, AWS Lambda, S3, RDS PostgreSQL, CloudWatch, AWS SNS, Great Expectations[cite: 5]

• Built an enterprise validation engine deploying 300+ rigorous assertions across 80+ distributed production tables, successfully cutting validation time by 85% (from 6 hours to 54 minutes)[cite: 5].
• Developed statistical anomaly detection routines (Z-Score, Isolation Forest) backed by automated schema validation, achieving 99% detection accuracy[cite: 5].
• Wired structural metadata tracking across 40+ production ETL pipelines, slashing root-cause incident investigation cycles by 75% (from 4 hours down to 1 hour)[cite: 5].

_{Managed with clean, declarative infrastructure. Open to high-impact distributed systems and real-time core platform engineering challenges.}