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Nitheesh2325/README.md

Nitheesh Chanambatla

Nitheesh Chanambatla data engineering profile

I build reproducible data pipelines, analytical warehouses, validation systems, and geospatial event-processing workflows with reproducible tests, explicit data contracts, and measurable reconciliation.

Featured systems

1. NASA Earth Observation Event Intelligence Platform — Flagship

NASA FIRMS geospatial event-processing platform verified locally with 1,000,000 replay events using Spark and PostgreSQL/PostGIS, with measured throughput of 6,688.88 events/second.

AWS infrastructure is designed and locally validated, not deployed. A separate 10-million-event experiment verified deterministic generation and read-back; Spark processing at that scale did not complete because of local JVM memory limits. GitHub Actions passed.

2. Customer360 Analytics Warehouse — Supporting

PostgreSQL dimensional warehouse with 400,000 verified sales facts, customer and product SCD Type 2 processing, surrogate-key resolution, validation and quarantine, repeat-safe loading, and 20 executed analytical SQL queries. Automated tests and hosted CI passed.

Facts resolve the dimension version current at load time; event-time historical attribution is not implemented.

3. RetailSync Data Platform — Supporting

Deterministic local 100,000-order batch pipeline with Pandas validation, rejected-record persistence, transactional SQLite snapshot replacement, rerun reconciliation, three analytical SQL queries, charting, and logging. Six tests passed; hosted CI uses a 1,000-row full-pipeline fixture.

Full-snapshot local batch processing—not incremental ingestion, CDC, streaming, cloud orchestration, or production deployment.

Technical toolkit

  • Languages: Python, SQL
  • Processing: Apache Spark, Pandas
  • Databases: PostgreSQL, PostGIS, SQLite
  • Data engineering: ETL, dimensional modelling, SCD Type 2, validation, reconciliation, idempotency
  • Engineering tools: Git, GitHub Actions, Docker
  • Cloud: AWS infrastructure design and local validation

Engineering principles

  • Evidence before claims and measurable validation before scale statements
  • Reproducible execution with explicit data contracts and rerun behavior
  • Reconciliation and failure handling treated as part of the pipeline
  • Honest limitations and tradeoffs documented beside the implementation

Contact

Pinned Loading

  1. nasa-earth-observation-event-platform nasa-earth-observation-event-platform Public

    Production-grade NASA Earth Observation data platform verified end to end at 1M events

    Python

  2. customer-360-analytics-warehouse customer-360-analytics-warehouse Public

    Verified PostgreSQL dimensional warehouse with SCD Type 2 processing, validation, repeat-safe loading, and 400,000 sales facts.

    Python

  3. retailsync-data-platform retailsync-data-platform Public

    End-to-end Data Engineering pipeline using Python, Pandas, SQLite, SQL, data quality validation, ETL processing, analytics, and logging.

    Python