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Chinmoy Das
Mid-Level Full-Stack Software Engineer specializing in AI agents and cloud platforms
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Backend/data engineer focused on climate/emissions data platforms, building production Python (FastAPI) microservices and AWS serverless/ETL pipelines (Glue/Athena/Lambda/EventBridge). Demonstrated strong reliability and observability practices plus measurable optimization wins, including cutting PostgreSQL query runtimes from minutes to seconds and reducing AWS costs from ~$6k/month to ~$400/month.
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Key Strengths
- Built and maintained production FastAPI microservice with PostgreSQL for carbon/emissions management platform
- Implemented reliability patterns: token auth with role checks, strict timeouts, exponential-backoff retries, idempotent request handling, async job offloading
- Production observability: structured JSON logs with request/user IDs and latency; CloudWatch dashboards/metrics for error rates, retries, job durations, slow queries
- Delivered serverless AWS data workflows (Lambda + EventBridge + S3 + RDS) with CI-based deployments and environment-specific config/secrets
- Major cost optimization impact: reduced monthly AWS spend from ~$6,000 to ~$400 via right-sizing, event-driven design, and query/ETL optimizations
- ETL expertise with AWS Glue/Athena: explicit schemas, table versioning, data quality filtering, fail-fast logging/alerts, and safe reprocessing from S3
- Modernized legacy spreadsheet/ad-hoc SQL reporting into API-driven services using parallel runs and stakeholder sign-off to validate parity
- Resolved pipeline incident caused by upstream schema changes/duplicate columns by enforcing schema validation, deduping columns, reprocessing data, and making reruns idempotent
- SQL performance tuning in PostgreSQL using execution plans, indexing, partitioning, materialized views, and pre-aggregations (minutes to seconds)
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