Chinmoy Das

About

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.

Experience

Education

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)