Abhiraj Karpe
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// PRODUCTION DEPLOYMENTS & ARCHITECTURAL CASE STUDIES · SECTION 04

Flagship Systems & Production Impact

Technical architecture breakdowns of production systems, projects, and repositories this candidate has attached to their dossier.

// INTERVIEW ENGINE

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SYSTEM 01 // PROJECTRole: FEATURED PROJECT

CDC Lakehouse Pipeline

Built an end-to-end CDC pipeline streaming PostgreSQL inserts, updates, and deletes through Debezium and Kafka into Apache Iceberg tables with 1.03s P95 latency. Integrated MinIO, Hive Metastore, and Trino for queryable lakehouse analytics; containerized a 7-service stack for reproducible deployment.

TechnologyPostgreSQL
StackDebezium
PlatformApache Kafka
Outcome1.03s P95 latencySelf-reported
Metric 02—Pending
Metric 03—Pending
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SYSTEM 02 // PROJECTRole: FEATURED PROJECT

Databricks Taxi Analytics Pipeline

Built an incremental Bronze–Silver–Gold pipeline processing 355M+ taxi trip records with schema normalization, data quality filtering, and deduplication. Designed partition-aware incremental queries that cut data scanned by 89% and execution time by 33%, producing 3 analytics-ready Gold datasets.

TechnologyDatabricks
StackPySpark
PlatformDelta Lake
Outcomecut data scanned by 89% and execution time by 33%Self-reported
Metric 02—Pending
Metric 03—Pending
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SYSTEM 03 // PROJECTRole: FEATURED PROJECT

Real-Time Crypto Data Engineering Pipeline

Built a real-time pipeline streaming CoinGecko market events through Kafka into Amazon S3 and Snowflake via Snowpipe. Developed incremental dbt transformations with MERGE-based upserts across RAW, STAGING, and GOLD layers, orchestrated with Airflow.

TechnologyApache Kafka
StackAWS S3
PlatformSnowpipe
Metric 01—Pending
Metric 02—Pending
Metric 03—Pending
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// INTERVIEW ENGINE

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// RECRUITMENT & ADVISORY ENGAGEMENTS

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