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With 7+ years of experience, I'm Expert in Web Scraping, Data Engineer, AI/ML and Full-Stack Developer specializing in large-scale data extraction, automation, and pipeline engineering. I build robust, scalable systems that transform raw data into actionable insights. 💡 Core Expertise Web Scraping & Automation: Expert in bypassing anti-bot systems (CAPTCHA, rate limits, IP rotation) using Scrapy, BeautifulSoup, Selenium, Playwright, and rotating proxies. Automation & Workflow Engineering: Airflow, Prefect, Dagster, n8n, Zapier, Make, Power Automate, UiPath, Step Functions, Logic Apps, GCP Workflows, Business Process Automation, RPA, CI/CD, Jenkins, GitHub Actions, GitLab CI/CD, Monitoring & Alerting. Data Engineering: Designing and building scalable ETL/ELT pipelines for structured, semi-structured, and unstructured data using Apache Airflow, Apache Spark (PySpark), Pandas, Dask, Databricks, Snowflake, Apache Kafka, Apache Hive, Apache Hadoop, Delta Lake, Apache Iceberg, dbt, AWS Glue, Azure Data Factory, Google Cloud Dataflow, Apache NiFi, Trino, Presto, and Apache Beam. Experienced in data warehousing, data lakes, lakehouse architectures, data modeling, data transformation, data quality, data governance, batch and real-time processing, streaming data pipelines, orchestration, workflow automation, schema design, partitioning, optimization, and performance tuning. Proficient with cloud platforms including AWS, Azure, and GCP, S3, Redshift, EMR, Athena, Lambda, Azure Synapse Analytics, Azure Data Lake Storage, BigQuery, Cloud Storage, and Pub/Sub. Skilled in SQL, Python, data integration, data migration, CDC, metadata management, monitoring, CI/CD, Docker, Kubernetes, and modern data stack technologies. Backend Development: High-performance APIs and microservices with FastAPI, Django, Flask, and Celery for async task handling. AI/ML Integration: Leveraging NLP and LLMs (LangChain, Llama, NLTK) for data enrichment, classification, and intelligent automation. Cloud & DevOps: Deploying scalable scrapers and data workflows on AWS (Lambda, ECS, S3), GCP, Docker, and Kubernetes. 🛠️ Tech Stack Data & Scraping: ▸ Scrapy | Selenium | Playwright | Proxies (BrightData, ScraperAPI, etc) ▸ Pandas | PySpark | Apache Airflow | PostgreSQL | MongoDB | Redis Backend & Cloud: ▸ Python (FastAPI, Django, Flask) | Celery | RabbitMQ ▸ AWS (Lambda, ECS, RDS, S3) | GCP | Docker | Kubernetes AI/ML: ▸ NLP (NLTK, spaCy) | LLMs (LangChain, OpenAI, Llama) | Data Annotation Let's turn your data challenges into reliable, scalable solutions. Send me a message to discuss your project!

I'm a Senior Data Engineer with 8+ years of strong technical expertise in building reliable and scalable data infrastructure, from data ingestion to transformation to warehousing, streaming, and data analytics, specializing in dbt, Snowflake, Airflow, Databricks (and more) across AWS, Azure, and GCP, with robust ELT and ETL pipelines. If your data pipelines are brittle, your data warehouse is slow, or your data was never built to scale, that is exactly what I fix, with fault tolerance, observability, and audit-ready quality engineered in from day one. I cover the full data engineering lifecycle: batch and real-time data pipelines, Modern Data Stack builds, lakehouse architecture, cloud and warehouse data migration, governance, and the data foundations that feed modern systems. 🎯 Core Expertise: ✅ Data Pipelines & Orchestration: End-to-end batch and real-time pipelines with Apache Airflow, Dagster, Prefect, and Azure Data Factory. Idempotent, schema-drift tolerant, and monitored so failures surface before they reach your stakeholders. ✅ Cloud Warehousing & Lakehouse: Snowflake, BigQuery, Amazon Redshift, Databricks, and Microsoft Fabric, with Delta Lake and Apache Iceberg lakehouse foundations, Medallion Architecture, partitioning, and performance tuning. ✅ Data Transformation & Modeling: dbt (Core and Cloud), SQLMesh, Spark and PySpark, Star Schema and dimensional modeling, analytics engineering best practices, full test coverage, and CI/CD for data models. ✅ Streaming & Real-Time Analytics: Distributed streaming with Apache Kafka, Flink, Spark Structured Streaming, Kinesis, and Pub/Sub, including exactly-once semantics, dead-letter queues, CDC, and end-to-end latency guarantees. ✅ Data Ingestion & Integration: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, and custom CDC pipelines for near-real-time sync across structured, semi-structured, and unstructured sources. ✅ Data Quality, Governance & Observability: Automated data quality frameworks, SLA monitoring, auditable lineage, data catalog and metadata management, and observability that catches bad data early. ✅ Cloud Migration & Modernization: Zero-downtime migration handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime, replacing brittle ETL and ELT with a clean Modern Data Stack. ✅ AI-Ready Data Infrastructure: Pipelines engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ------------------------------------------------------ ⚙️Tech Stack: ⚡ Warehouses & Lakehouse: Snowflake | BigQuery | Redshift | Databricks | Microsoft Fabric | Delta Lake | Iceberg ⚡ Transformation: dbt | SQLMesh | Spark | PySpark | Star Schema | Medallion Architecture ⚡ Orchestration: Airflow (GCP Cloud Composer and AWS MWAA) | Dagster | Prefect | Azure Data Factory ⚡ Streaming: Kafka | Flink | Kinesis | Pub/Sub | Spark Structured Streaming | ClickHouse ⚡ Ingestion: Fivetran | Airbyte | Matillion | Stitch | Hevo | Meltano | CDC ⚡ Cloud: AWS | GCP | Azure ⚡ Languages: Python | SQL (Snowflake, BigQuery, T-SQL, PL/pgSQL) | FastAPI ⚡ Databases: PostgreSQL | MySQL | SQL Server | DynamoDB | MongoDB ⚡ BI & Reporting: Looker | Tableau | Power BI | GA4 | Metabase | Superset | Streamlit | Grafana ------------------------------------------------------ ⭐ What Clients Say: 🏅 "Adarsh rebuilt our analytics pipeline on Snowflake, Airflow, and dbt, giving us reliable, version-ready data. Reporting accuracy improved overnight, and we can finally trust the numbers." – Anita, Head of Product, FinTech SaaS 🏅 "He designed a zero-downtime migration to a modern data warehouse that cut query latency by more than half while keeping our SLAs intact." – Daniel, VP of Data, AdTech Firm 🏅 "Clean architecture, solid dbt models, and Airflow pipelines running without issues for months. He brought a level of engineering discipline we hadn't seen from a data consultant before." – Mark, Director of Data Engineering, E-commerce Startup 🏅 "We came to him with a Spark pipeline costing us a fortune and delivering stale data. He restructured the workflow logic and cut processing time by 70%." – Leo, Head of Analytics, HealthTech SaaS ------------------------------------------------------ 🏆 TOP RATED PLUS | EXPERT-VETTED | Top 1% on Upwork | 8+ Years Experience | 100% Job Success 🚀 Ready to build a scalable, production-ready data infrastructure to turn your raw data into reliable, actionable business insights? Click the 'Invite to Job' button on the top right, and let's discuss your data pipeline!

I'm a Senior Data Engineer with 4.5+ years of experience building scalable, cloud-native data platforms that turn raw data into reliable, business-ready insights. I've delivered enterprise solutions across banking (NAB), healthcare (Molina), and CPG (PepsiCo), specializing in end-to-end pipeline architecture, data modeling, and cloud migrations. What I bring to your project: 🔹 Cloud Data Engineering – Deep expertise in Azure (Databricks, Data Factory, Synapse) and AWS (EMR, Glue, S3, RedShift), with hands-on migration experience from on-prem and Teradata to cloud. 🔹 Pipeline Architecture & ETL – I design and build robust ingestion frameworks handling batch, incremental, and real-time data (Event Hub, Kafka) across formats like JSON, CSV, Parquet, and fixed-width files. 🔹 Data Modeling & Warehousing – Skilled in dimensional modeling, Data Vault, star/snowflake schemas, and silver/gold layer design. I've modeled 50+ tables across Oracle Fusion, SAP S/4, and healthcare domains. 🔹 Transformation & Orchestration – I translate complex business rules into DBT models, orchestrate workflows with Apache Airflow or AutoSys, and automate CI/CD via Jenkins and Azure DevOps. 🔹 Performance & Governance – I tune PostgreSQL and Spark jobs, implement data quality checks, reconciliation frameworks, and ensure compliance with data governance standards. 🔹 Generative AI & MLOps – Databricks-certified in Generative AI, with experience integrating MLflow for experiment tracking and building LLM-based automation using OpenAI and LangChain. Tech Stack: Python | SQL | Scala | Apache Spark | DBT | PostgreSQL | Snowflake | Airflow | Databricks | Azure | AWS | Git | Jenkins | MLflow | Power BI Certifications: Databricks Certified Data Engineer Professional | Azure Data Engineer (DP-203) | Snowflake SnowPro Core | Fabric Analytics Engineer (DP-600) | Generative AI Engineer Associate Whether you need a production-grade pipeline, a cloud migration, or a well-modeled data warehouse, I deliver clean, documented, and scalable solutions — on time and with clear communication. Let's discuss your project!

Data Engineer & AI Developer | 3+ Years Financial Industry Experience I build data pipelines, AI-powered applications, and automation systems that run reliably at scale. My background spans web scraping, LLM integration, computer vision, betting automation, and full-stack data dashboards — delivered to clients across the US, UK, Europe, and Japan. 💼 Background — 3+ years at a leading Indian bank building risk models, credit scorecards, and AutoML pipelines — PG Diploma in Big Data Analysis ⚡ What I Deliver — Web scrapers handling 1.2M+ URLs and 120K daily pipelines — LLM/AI apps using GPT-4, Gemini, LangChain, RAG, Text-to-SQL — Full Betting automation for horse racing, golf, and football signals — Computer vision pipelines with YOLOv8 and PaddleOCR — Streamlit dashboards, risk scorecards, and AutoML tools 🏆 Notable Work — PitchBook scraper — 1.2M URLs — Njuskalo — 120K daily real estate listings — Text-to-SQL architecture — BetFare — full Betfair automation — LLM Notebook — $1,420 solo delivery — Anti-bot bypass systems 🛠️ Stack Python · Playwright · Selenium · GPT-4 · Gemini · LangChain · Streamlit · PySpark · SQL · YOLOv8 · PaddleOCR · FastAPI · Betfair API · n8n Clean code. Clear communication. Delivered on time.

Hello, I'm Anup Patil, an AI-powered Web Scraping & Data Engineer with 12+ years experience in building intelligent data extraction systems, automation pipelines, and dashboards for AI/ML and business intelligence projects. Today, I leverage modern AI coding tools (Codex, Claude, Cursor, Copilot) together with strong Python expertise to deliver faster, scalable, and cost-efficient solutions for complex data problems. I help companies collect, structure, and transform data into usable formats for Machine Learning, analytics dashboards, and business automation. 🚀 Core Expertise 🤖 AI-Powered Web Scraping Intelligent scraping using LLMs + Python Dynamic websites, login systems, pagination, JS rendering Scrapy, Selenium, Playwright, Requests, BeautifulSoup Automatic data cleaning and structuring for AI usage 🧠 AI & LLM Automation GPT / Claude powered workflows AI agents for decision making pipelines prompt engineering & structured output pipelines automated classification & enrichment of datasets 📊 Data Engineering for ML Projects dataset preparation for ML models data normalization and transformation structured JSON, CSV, database pipelines large-scale scraping (millions of records) 📈 Dashboard & Web Apps for Data Projects dashboards for scraped or AI processed data FastAPI / Django APIs Next.js dashboards data visualization pipelines ⚙️ Automation Systems workflow automation using Python & APIs n8n, cron jobs, server automation PDF parsing, document extraction business process automation ☁️ Cloud & Infrastructure AWS deployment (EC2, S3, Lambda) proxy rotation, captcha solving scalable scraping architecture Docker based deployments 💡 Example Projects I Can Build AI-powered lead generation scraper ML training dataset collection pipelines automated competitor price monitoring document & PDF data extraction systems AI classification pipeline for companies or products dashboard showing real-time scraped insights automation bots for repetitive online tasks 🛠 Tech Stack Python • Scrapy • Selenium • Playwright • FastAPI • Django LLM APIs • OpenAI • Claude • Prompt Engineering Next.js • API Integration • PostgreSQL • MongoDB AWS • Docker • Proxy rotation • CAPTCHA solving AI Tools: Codex • Cursor • Copilot • Claude • ChatGPT ⭐ Why hire me? ✔ Combine 12 years scraping experience + modern AI tools ✔ faster development using AI-assisted coding ✔ scalable architecture for large datasets ✔ clear communication & reliable delivery ✔ long-term collaboration mindset If your project involves AI data collection, automation, or ML dataset preparation, I can help you build a reliable system quickly. 📩 Send me a message — I can suggest best architecture for your project.

⚡ Data pipelines breaking, slow, or too expensive? That’s exactly what I fix. I help companies turn messy, unreliable data systems into scalable, high-performance pipelines — so teams can actually trust their data and move faster. Over the past few years, I’ve built and optimized data platforms handling 50GB/day streams to 500GB+ processing workloads, across ecommerce, fintech, real estate, and SaaS. 🔑 What I Deliver (Real Outcomes, Not Just Tools) 🔹 Robust ETL/ELT Pipelines: Built on AWS Glue, Azure Data Factory, Databricks, Airflow → Designed for zero-failure execution & recovery-safe architecture 🔹 Cloud Data Warehousing: Redshift, Snowflake, BigQuery, Azure Synapse → Structured for fast queries, low cost, and scalable analytics 🔹 Real-Time & Batch Processing: PySpark, Kafka, Delta Lake → Handling millions of records daily with optimized latency 🔹 AI-Ready Data Foundations: → Clean, structured pipelines ready for ML models, LLMs, and BI tools. 🔹 Performance Optimization: → Reduced processing time by 50% on production workloads → Optimized queries & pipelines saving compute cost + runtime 🔹 Business Intelligence Layer: Power BI, Looker Studio → Dashboards that leadership teams actually use for decisions. 📦 Proven at Scale:- 🏪 Ecommerce Data Platform (1M+ stores | 500K customers): Built end-to-end ETL pipelines using AWS Glue Implemented incremental (delta) processing across multiple data sources Designed Redshift warehouse for high-volume analytics Delivered Power BI dashboards for business insights ✅ Enabled AI-driven personalization for 100K+ active users ✅ Reduced manual data dependency across teams 💳 FinTech / Freight Billing AI Platform: Designed data infrastructure for AP/AR automation Integrated ERP + TMS systems via APIs Built real-time dashboards + audit tracking ✅ Improved financial visibility & reconciliation accuracy ✅ Enabled automated decision-making for cash flow optimization 🏠 Real Estate Data Intelligence Platform: Built pipelines ingesting: Zillow Realtor HUD Census Crime datasets Centralized into PostgreSQL + Azure Data Warehouse ✅ Computed ROI, NOI, Cap Rate, Cash Flow at scale ✅ Enabled investment decision analytics 📊 Retail Location Intelligence (Databricks / Snowflake): Processed ~500GB mobile location data Built anomaly detection using moving averages ✅ Automated detection of: data drops duplicates false entries 🔧 Logistics Optimization (Azure): Replaced upsert with SQL MERGE ✅ Reduced processing time by 50% ✅ Achieved zero data loss in production pipelines 📡 Wellness Data Platform: Consolidated fragmented APIs into unified pipelines ✅ Fixed data failures on 250MB+ tables (~1GB compute load) ✅ Improved data consistency and pipeline reliability