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Need a data platform that works in production, not just on a whiteboard? I design and build end-to-end data systems that turn fragmented raw data into trusted analytics and AI-ready infrastructure. 10+ years experience. Founder of Vyntics. Delivered consulting solutions for AT&T, Patreon, Jumio & Acko. WHAT YOU GET: Reliable ETL/ELT pipelines (batch + streaming) that keep dashboards accurate and stop 2 AM debugging Cloud data platforms (AWS/GCP) optimized for scale, cost control, and long-term maintainability Production-grade AI/RAG systems: accurate retrieval, eval pipelines, and scalable deployment Legacy-to-cloud migrations with zero-downtime cutovers and built-in validation frameworks PROVEN IMPACT: Migrated enterprise warehouse to BigQuery: 40% lower query costs, zero downtime Built Databricks lakehouse (Delta + Unity Catalog) for governed self-service analytics Designed Snowflake + dbt architecture that cut ELT dev time by 60% Deployed RAG systems on real-world data with measurable accuracy and latency improvements HOW I WORK: Flexible engagement: I can architect your system in a focused 2-week discovery sprint, or lead full end-to-end delivery via my Vyntics team. Always production-first, cost-aware, and documented for your team's long-term success. BEST FIT FOR: Startups scaling infrastructure | Companies migrating legacy systems | Teams adding AI/RAG | Leaders who want clarity before heavy investment Evaluating your data strategy or stuck on architecture decisions? Message me with your challenge. I will reply with 2-3 actionable next steps, no obligation. Arun Mudgal Founder & Principal Consultant, Vyntics

Struggling to unlock value from your data or build scalable, high-performance analytics platforms? I’m 𝑯𝒂𝒔𝒆𝒆𝒃 𝑨𝒔𝒊𝒇,a Senior Data Engineer specializing in Databricks, Snowflake, Big Data Engineering, and scalable ETL/ELT solutions. With expertise in PySpark, Python, SQL, GCP, AWS, Azure, and NLP, I build high-performance data pipelines, cloud data platforms, and real-time analytics solutions. Experienced in data warehousing, cloud integration, machine learning workflows, and performance optimization to transform raw data into actionable business insights. Let’s build reliable, scalable, and data-driven solutions for your business growth. I’ve successfully completed 99+ projects across industries, designing ETL pipelines, MLOps workflows, Delta Lake architectures, and cloud analytics solutions on AWS, Azure, and GCP. ✔️ 𝑯𝒐𝒘 𝑰 𝑯𝒆𝒍𝒑 𝑩𝒖𝒔𝒊𝒏𝒆𝒔𝒔𝒆𝒔 𝑻𝒓𝒂𝒏𝒔𝒇𝒐𝒓𝒎 𝑫𝒂𝒕𝒂 𝒊𝒏𝒕𝒐 𝑰𝒏𝒔𝒊𝒈𝒉𝒕𝒔 ➜ Databricks & Big Data Engineering I specialize in designing enterprise-grade Databricks Lakehouse architectures and Delta Lake solutions. My expertise in Spark and PySpark allows me to build high-performance pipelines for both batch and real-time analytics, ensuring your data infrastructure is robust and scalable. ➜ Machine Learning & MLOps With a focus on machine learning and MLOps, I build and deploy predictive models using tools like MLflow and TensorFlow. I automate end-to-end ML pipelines to enhance efficiency and accuracy, driving impactful insights from your data. ➜ Cloud & Data Platforms I implement secure, scalable cloud solutions on platforms like AWS, Azure, and GCP. My experience includes cloud migration, Kubernetes, Docker, and CI/CD automation, ensuring seamless integration and optimal performance. ➜ ETL & Data Pipelines I develop reliable ETL processes and data pipelines that streamline data integration and transformation. My work with streaming analytics using Kafka and Spark ensures real-time data processing and actionable insights. ➜ Data Analyst & Visualization I create actionable dashboards and visualizations using Power BI, Tableau, and Databricks SQL. My focus is on driving KPI reporting and business intelligence to support strategic decision-making. ➜ Snowflake I leverage Snowflake's capabilities to build efficient data warehousing solutions, optimizing data storage and retrieval for enhanced performance and scalability. ➜ Python My proficiency in Python allows me to develop complex data processing scripts and machine learning models, ensuring robust and efficient data handling. ➜ NLP (Natural Language Processing) I apply NLP techniques to extract meaningful insights from unstructured data, enabling advanced text analytics and improved decision-making processes. ➜ GCP (Google Cloud Platform) I utilize GCP's powerful tools to design and deploy scalable cloud solutions, ensuring high availability and performance for your data-driven applications. ➜ Data Warehouses I design and manage data warehouses that provide a centralized repository for your data, facilitating efficient data analysis and reporting. ✔️ 𝑲𝒆𝒚 𝑻𝒐𝒐𝒍𝒔 & 𝑻𝒆𝒄𝒉𝒏𝒐𝒍𝒐𝒈𝒊𝒆𝒔 ▪ Databricks & Big Data: Databricks, Delta Lake, Apache Spark, PySpark, Unity Catalog, Kafka, Hadoop, Real-time Streaming ▪ Machine Learning: MLflow, TensorFlow, PyTorch, scikit-learn, Feature Store, Predictive Analytics, NLP ▪ Cloud Platforms: AWS, Azure, GCP, Kubernetes, Docker, CI/CD ▪ Analytics & BI: Power BI, Tableau, Databricks SQL, KPI Dashboards, Data Strategy ▪ Data Engineering: ETL Pipelines, Data Lakes, Data Warehousing, Data Migration, Performance Optimization ✔️ 𝑾𝒉𝒚 𝑪𝒉𝒐𝒐𝒔𝒆 𝑴𝒆 I combine deep technical expertise with practical business understanding, delivering scalable, cost-efficient, and AI-ready data solutions. My goal is to turn your data into a strategic asset that powers smarter decisions and measurable growth. Let’s collaborate to build your next-generation analytics platform and unlock the full potential of your data. Check my portfolio for architecture samples, dashboards, and case studies. Databricks Engineer, Big Data Consultant, Spark Developer, MLOps Engineer, Data Engineer, AWS Data Specialist, Azure Databricks, GCP Analytics, ETL Developer, Data Analytics, Delta Lake Expert, Machine Learning Engineer, Python, Database Architecture, Data Processing, ETL, Big Data, Database Design, Data Engineering, Data Analytics & Visualization Software, Data Visualization, Deep Learning Modeling, Data Warehousing & ETL Software, Snowflake, Amazon Web Services, ETL Pipeline, Machine Learning, Deep Learning, Data Science, Data Analysis, Cloud Engineering, Artificial Intelligence, Databricks Engineer, Big Data Consultant, Spark Developer, MLOps Engineer, Data Engineer, AWS Data Specialist, Senior Data Engineer specializing in Databricks, Snowflake, Big Data Engineering, and scalable ETL/ELT solutions. With expertise in PySpark, Python, SQL, GCP, AWS, Azure, and NLP

🏆 TOP RATED PLUS || Top 1% on Upwork || 8+ Years of Experience || 100% Job Success || Expert Vetted Most data teams are held back by unreliable pipelines, untrustworthy warehouses, and data infrastructure never built to scale. That's exactly what I fix. As a Senior Data Engineer, I don't just write SQL and call it a pipeline. I architect end-to-end data systems where reliable ingestion feeds into clean, versioned transformations that power decisions your business can act on. My approach prioritizes fault tolerance, scalability, and observability across both batch processing and real-time analytics workloads. This ensures your data infrastructure is not just functional, but resilient and audit-ready. Whether you need cloud data migration, data platform modernization to a Modern Data Stack (Snowflake/dbt/Airflow, Microsoft Fabric), or streaming analytics infrastructure, I deliver production-grade systems that help technical founders and data teams eliminate pipeline debt, automate complex data workflows, and build scalable infrastructure ready for AI workloads. ------------------------ Where I make the biggest impact: ✅ I lead data migration and data platform modernization projects, replacing brittle ETL and ELT pipelines with a Modern Data Stack built on Snowflake, dbt, Airflow, and Microsoft Fabric. ✅ Every engagement includes Medallion Architecture design, full test coverage, CI/CD for data models, data lineage tracking, and documentation that outlasts the project. ✅ I design data pipelines for both batch processing and real-time analytics, idempotent, schema-drift tolerant, and monitored through data observability frameworks, so failures are caught before they reach your stakeholders. ✅ Warehouse models are built to serve the business: Star Schema, dimensional modeling, dbt projects, analytics engineering best practices, and a metrics layer backed by a data catalog and metadata management. ✅ I architect distributed systems for big data and streaming analytics, including Kafka, Flink, Spark Structured Streaming, exactly-once semantics, dead-letter queues, and end-to-end latency guarantees. ✅ AI data pipelines are engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ✅ I bring governance to data platforms through data mesh, data catalog implementation, metadata management, and data integration across systems. ✅ Data quality and data reliability are enforced end to end, with automated frameworks, SLA monitoring, auditable lineage, and observability that catches bad data before it reaches your stakeholders. ✅ I build AI-ready data infrastructure and lakehouse foundations, Delta Lake, Apache Iceberg, cloud data architecture, and CDC pipelines for near-real-time sync. ✅ Cloud data migration is handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime. ------------------------ What I Build With: 🗄️ Warehouses, Lakehouses & Data Lakes: Snowflake, BigQuery, Redshift, Databricks, Microsoft Fabric, Delta Lake, Iceberg ⚙️ Transformation: dbt (Core & Cloud), SQLMesh, Spark, PySpark, Star Schema, Medallion Architecture 🔁 Orchestration: Airflow, Dagster, Prefect, Azure Data Factory, Microsoft Fabric 📨 Streaming: Kafka, Kinesis, Pub/Sub, Flink, Fabric Eventstream 🔗 Ingestion: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, CDC pipelines ☁️ Cloud: AWS, GCP, Azure 🐍 Languages: Python, SQL (SF, BQ, 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 ⭐ "Adarsh built our entire data platform from the ground up. Clean architecture, solid dbt models, and Airflow pipelines that have been 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 Adarsh with a Spark pipeline that was costing us a fortune and delivering stale data. He identified the bottlenecks, restructured the workflow logic, and reduced our processing time by 70%. Technically sharp, communicates clearly, and delivers without hand-holding." – Leo, Head of Analytics, HealthTech SaaS ------------------------ 🚀 Let's Build Your Data Foundation. If your data infrastructure needs to be faster, cleaner, and trustworthy, send a quick message about your project, and I'll take it from there.

A failing data pipeline is usually not the complete problem. The deeper issue may be poor architecture, inconsistent source data, missing validation, weak orchestration, slow SQL models, uncontrolled cloud costs, or a platform that was never designed to scale. I help companies identify the real constraint and build a data system that is reliable, observable, scalable, and useful to the business. With 8+ years of experience across data engineering and cloud infrastructure, I support organizations that need to: 📉 Eliminate recurring pipeline failures ⏱️ Reduce reporting delays and manual processing 🔗 Integrate disconnected applications, APIs, and databases 📊 Create trustworthy datasets for dashboards and analytics ☁️ Modernize legacy infrastructure in AWS, GCP, or Azure 🤖 Prepare structured data for machine learning and AI 💰 Improve performance without wasting cloud resources 🚀 How I Solve Data Problems 🔎 Discovery and Diagnosis I review your existing architecture, data sources, pipelines, reporting requirements, failure points, processing volumes, and business priorities. The objective is to determine whether the real bottleneck is ingestion, transformation, modeling, orchestration, infrastructure, data quality, or downstream reporting. 🏗️ Architecture and Implementation Based on the diagnosis, I design a practical solution that may include: * Batch or real-time ingestion pipelines * Cloud data lakes, warehouses, or lakehouses * ETL/ELT orchestration and automation * Dimensional and analytics-ready data models * Data validation and quality controls * Monitoring, alerts, retries, and failure recovery * Infrastructure as Code and CI/CD * Performance and cost optimization 🛡️ Stabilization and Accountability Delivery does not end when the code runs once. I focus on production readiness through documentation, testing, logging, monitoring, deployment processes, ownership clarity, and maintainable architecture. 🛠️ Technical Expertise Python | SQL | PySpark | Apache Airflow | Apache Spark | Kafka | dbt | Databricks | AWS Glue | Google Dataflow | BigQuery | Snowflake | Redshift | Azure Synapse | Docker | Kubernetes | Terraform | GitHub Actions | Power BI | Tableau | MLflow 💼 What You Receive ✅ A clear understanding of the root problem ✅ An architecture aligned with your actual requirements ✅ Reliable and maintainable production pipelines ✅ Clean, validated, analytics-ready datasets ✅ Monitoring and visibility into pipeline health ✅ Documentation your internal team can understand ✅ A scalable foundation for reporting, automation, and AI I work as a technical partner not an order taker. Instead of simply implementing the first tool requested, I help determine what should be built, why it should be built, and how it will improve reliability, speed, cost, or decision-making. 📩 Share your current challenge, architecture, and expected outcome. I’ll help you identify the most important issue to solve first.

Messy data slowing your team down? I build scalable ETL/ELT pipelines and modern cloud architectures on Azure, Databricks, Fabric, and Snowflake that turn raw, chaotic data into clean, analytics-ready systems fast and reliably. I bridge the gap between fragmented data sources and production-grade dashboards, seamlessly adapting to your existing infrastructure rather than forcing an expensive rebuild. What I Can Help You With: Data Warehouse & Lakehouse Architecture: Implementing Medallion design patterns (Bronze → Silver → Gold) using Delta Lake, Microsoft Fabric OneLake, and Snowflake. Scalable ETL/ELT Ingestion: Building automated, metadata-driven pipelines via Azure Data Factory, Fabric Pipelines, Databricks (PySpark/SQL), and dbt. Real-Time Data Streaming: Architecting low-latency workflows using Apache Kafka, Azure Event Hubs, and streaming engines. Database Design & Optimization: Performance tuning, indexing, and data modeling for PostgreSQL, Azure SQL, and cloud warehouses. Proven Project Highlights: Microsoft Fabric Incremental Pipeline: Built a control-table pattern using Get Metadata, Lookup, and ForEach loops to orchestrate zero-duplicate, quarterly ingestion from SharePoint into OneLake via Dataflow Gen2. Azure/Databricks Streaming: Developed a restaurant analytics platform processing 80,000+ events/day, cutting reporting lag from 6 hours to under 3 minutes. Kafka/Snowflake Pipeline: Engineered a real-time stock market data pipeline tracking 120+ tickers with under 8 seconds end-to-end latency. I write clean, documented code your team can maintain long-term and provide transparent daily updates. Message me with your data challenge and I’ll walk you through exactly how to solve it.

Certified Data & AI Engineering consultant with Expertise in developing Data Pipelines for Data warehousing, Data Lake, Data LakeHouse, and Data Analytics. That's been my daily work across 40+ Upwork contracts. Are you looking to make data-driven decisions and improve your business processes? Let's work together to unlock the power of your data and turn it into actionable insights for your business. As a data professional with 7+ years of experience building data pipelines and Lakehouse architectures on Azure, Aws and Databricks, mostly for healthcare and e-commerce clients, though I've worked across fintech and supply chain too. ADF / Databricks workflows for orchestration, Delta Lake for storage, Unity Catalog for governance. Some clients come with a greenfield project: a new Lakehouse to design, a pipeline to build from scratch, a migration off on-prem into Azure or Aws. Others come with something broken that needs fixing. Either way, I've done both enough times across healthcare, e-commerce, fintech, and supply chain to know what works and what doesn't. What I've built for clients: 🏥 Healthcare: Inherited years of fragmented EMR data with no reliable reconciliation. Built a bronze-silver-gold Databricks Lakehouse with composite key design and a daily Delta Lake MERGE-based reconciliation framework. Reduced pipeline processing time by 80% and cleared a multi-year backlog the previous system couldn't handle. 🛒 E-commerce: Unified fragmented sales and marketing data from Shopify, HubSpot, and Google Analytics into a single Delta Lake. The business finally had a clean, trusted view of customer behaviour, sales trends, and campaign performance they could actually make decisions from. 🏦 Mortgage/Fintech: Terabytes of mortgage documents flowing in from 10+ lenders, each with their own XML schema that drifted without warning. Designed a canonical schema layer with versioned per-source mappings so schema changes upstream never broke downstream pipelines. Built on Azure Data Factory and Azure Data Lake at scale. My core stack: Databricks (Delta Lake, Unity Catalog, Databricks SQL) · Apache Spark · PySpark · SparkSQL · Microsoft Fabric · Azure Data Factory · Azure Synapse · ADLS · Python · SQL Available for fixed-price projects and long-term retainers. If you know what you need to build, let's talk scope. If you're still figuring it out, I'm happy to start there too.