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swapniltake1/README.md

👋 Hi — I'm Swap

Data Engineer (2.6+ years) — building scalable, production-grade data platforms on Azure and Databricks.


About

I design and deliver scalable data engineering solutions for analytics and ML. My focus areas are reliable ETL/ELT pipelines, Delta Lake-backed data lakes, PySpark performance tuning, and enterprise data delivery on Azure (ADF, Databricks, ADLS, Fabric). I combine engineering rigor (testing, monitoring, deployment) with a strong emphasis on data quality and cost-efficient architecture.

Key strengths:

  • Scalable ETL/ELT (batch and micro-batch) using PySpark and Databricks
  • Delta Lake architecture, partitioning, and schema evolution strategies
  • Azure ecosystem: Data Factory, Databricks, ADLS Gen2, Fabric
  • SQL performance optimization, data modeling (star schema), CDC and incremental loads
  • Production ops: CI/CD, monitoring, cost tuning, and observability
  • Integrating GenAI & RAG patterns into data workflows (embeddings, vector DBs)

Technical Highlights

Languages & Tools

Python • PySpark • SQL • Scala (basics) • Git • Bash

Big Data & Processing

Apache Spark • Databricks • Delta Lake • Parquet • Spark SQL • Structured Streaming

Cloud & Storage

Azure Data Factory • ADLS Gen2 • Azure Databricks • Microsoft Fabric

Architecture & Practices

Data Lake (Bronze/Silver/Gold) • CDC & Incremental Loads • Data Modeling (Fact & Dimension) • ETL Testing • Observability & Instrumentation

Real-time & Streaming

Apache Kafka • Structured Streaming • Micro-batch processing • Checkpointing & Exactly-once patterns

ML & GenAI Integration

Vector DBs • Embeddings • Prompt engineering • RAG pipeline design


Core Capabilities

  • ETL / ELT pipeline design and implementation (PySpark & Databricks)
  • Delta Lake design: partitioning, compaction, and schema evolution
  • Incremental processing & CDC
  • SQL query optimization & Spark performance tuning
  • Data validation, reconciliation and testing frameworks
  • CI/CD and operationalizing data pipelines

Certifications

  • Databricks Certified: Data Engineer Associate
  • Databricks Accreditation - AI Agent Fundamentals
  • Microsoft Certified: Fabric Data Engineer Associate
  • Microsoft Certified: Azure AI Fundamentals
  • Microsoft Certified: GitHub Copilot
  • Google Cloud Certified: Generative AI Leader
  • Google Cloud Certified: Cloud Digital Leader
  • HackerRank Certified: SQL (Advanced)

📊 Contribution Graph


Let's Connect


⭐ If you find my work interesting, consider starring my repositories! Thank you for visiting :)


🐍 Contribution Snake

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  1. rag-implementation-for-own-data rag-implementation-for-own-data Public

    RAG workflow using your own documents, Chroma vector storage, Gemini embeddings, and a local Docker-served LLM for offline-style inference

    Jupyter Notebook

  2. databricks-mosaic-ai-agent databricks-mosaic-ai-agent Public

    Step-by-step starter project demonstrating how to build, test, and operationalize a Mosaic AI Agent on Databricks using MLflow, Foundation Models, and tool execution.

    Python

  3. bmw-cars-data-engineering bmw-cars-data-engineering Public

    End-to-End Azure data engineering project for BMW car sales

    Jupyter Notebook

  4. CryptoStream-Real-Time-Market-Data-Engineering-Platform CryptoStream-Real-Time-Market-Data-Engineering-Platform Public

    An enterprise-grade, metadata-driven data platform for ingesting, processing, validating, and analyzing real-time cryptocurrency market data using the CoinGecko API and Databricks Lakehouse archite…

    Python 1 1

  5. Banking-Data-Platform-with-Batch-Streaming-Orchestration Banking-Data-Platform-with-Batch-Streaming-Orchestration Public

    end-to-end, enterprise-grade data engineering project designed to simulate real-world financial data processing systems.

    Python