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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, AWS Step Functions, 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 governed through the Glue Data Catalog and Lake Formation, with Athena and Redshift Spectrum for serverless queries, Medallion Architecture, partitioning, and performance tuning. ✅ Data Transformation & Modeling: dbt (Core and Cloud), SQLMesh, Spark and PySpark on EMR and AWS Glue, 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 | Athena | Delta Lake | Iceberg ⚡ Transformation: dbt | SQLMesh | Spark | PySpark | AWS Glue | EMR | Star Schema | Medallion Architecture ⚡ Orchestration: Airflow (GCP Cloud Composer and AWS MWAA) | Dagster | Prefect | Azure Data Factory | Step Functions ⚡ Streaming: Kafka | Flink | Kinesis | Pub/Sub | Spark Structured Streaming | ClickHouse ⚡ Ingestion: Fivetran | Airbyte | Matillion | Stitch | Hevo | Meltano | CDC ⚡ Governance & Catalog: Glue Data Catalog | Lake Formation | Unity Catalog | Microsoft Purview | Dataplex ⚡ 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!

🏆 𝗧𝗢𝗣 𝗥𝗔𝗧𝗘𝗗 𝗣𝗟𝗨𝗦 – 𝗔𝗺𝗼𝗻𝗴 𝘁𝗵𝗲 𝗧𝗼𝗽 𝟯% 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝗼𝗻 𝗨𝗽𝘄𝗼𝗿𝗸. 🏆 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

☑️ 𝗤𝘂𝗮𝗹𝗶𝘁𝘆-𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 ☁️ 𝗰𝗹𝗼𝘂𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 & 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 🔒 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗼𝗻𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗶𝗺𝗲. 🎯 10+ 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲, 80K 𝗨𝗦𝗗+ 𝗶𝗻 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗮𝗻𝗱 𝗺𝗮𝗻𝗮𝗴𝗲𝗱. 𝗘𝘅𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝗙𝗼𝗿𝘁𝘂𝗻𝗲 𝟱𝟬𝟬 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. ☑️ 📞 Invite me to your job, and we can book a complimentary 30-minute consultation together that’s earnestly helpful. 📞 Microsoft Fabric | MS Fabric | Azure Function | Azure Synapse | Azure Data Factory | Azure Key Vault | Azure APIM | Azure Analysis Services | Azure SQL Database | Azure Open API Azure Sentiment Analysis | Azure Large Language model | Azure Logic Apps | Azure Active Directory (Entra) | Azure data lake storage | Azure Blob Storage | Azure Cost Optimization | Cost Analyst Power BI | Mquery| DAX | Dax Studio | Tabular Editor | Perfomance Analyser | SQL Profiler You have the option to hire someone cheaper who will take 5 times longer to complete the job correctly. Alternatively, you can hire me, an expert, to efficiently deliver the perfect solution while you relax. Consider me your cloud solution, Advil – I consistently alleviate headaches rather than create them. ✅ What I do -: Take your raw data, process it, and present it in a meaningful and reproducible way, while keeping you aware of what's happening (in plain language, if you're a non-tech person) and working together with your team (if you already have a tech structure) on every step of the process - from data extraction to final presentation. -: I enjoy solving complex data issues with my skill set. My passion is data readiness and making data "work" for my clients. -: Performance Tuning: Performance tuning is what I love to do. I have worked on a few tables that had 1 billion records. I am an expert in table partitioning, including indexing and other related topics. ✅ Offering the services: -: Data analytics -: Full-stack Power BI solution development -: PowerQuery and M language programming -: Dashboard design and implementation -: Data Extraction -: Database Architect -: END-TO-END BI Solution Design -: Performance Tuning -: Table Partitioning -: Automation of almost everything in Bi Solution. ✅ Programming Languages -: Python, C#, Java, Google App Script, PowerShell, Batch Scripting, DAX, MDX ✅ Databases: -: Microsoft SQL Server (2008 to 2019) (Microsoft certified) -: Azure SQL Server -: Azure Data Warehouse -: Cosmos DB -: MySQL -: PostgreSQL -: Oracle -: MongoDB ✅ ETL -: SSIS (SQL Server Integration Services) -: ADF (Azure Data Factory) -: ADB (Azure DataBricks) -: AirByte -: AirFlow -: Domo -: Alteryx -: Tableau Prep Builder ✅ Reporting -: Microsoft Power BI -: Google Data Studio -: DevExpress -: DOMO -: Metabase ✅ Analytics -: SSAS (Multidimensional and tabular cube) -: Azure Data Bricks -: Azure Analysis Services -: DBT ✅ API Data Pull Implementation -: Shopify -: Magento -: NetSuite -: PeopleVox -: SmartLPA -: Tempo/Jira -: GitLab/GitHub ✅ Solutions for business: -: Real Estate -: Bar & Restaurants -: Manufacturing -: Human Resources -: Healthcare -: Marketing -: Project management -: Sales -: Finance -: Media and Advertisement

I bring extensive hands-on experience in the realm of data science, showcasing proficiency in various Hadoop components such as MapReduce, Hive, Pig, alongside a deep understanding of AWS cloud services. Over the course of my career, I have successfully executed numerous projects utilizing machine learning techniques for in-depth data analysis. Specifically, I leverage Apache Spark to efficiently process vast datasets for analytical purposes. My expertise extends to the full spectrum of Spark's capabilities, including Spark Streaming, Spark MLlib, and Spark GraphX, which have proven instrumental in enhancing the speed and scalability of data processing in various projects. I have implemented Spark MLlib to develop machine learning models tailored to meet specific client requirements, focusing on prediction and classification tasks. In my current role, I am deeply involved in working with Hadoop components, and I continue to harness the advanced features of Spark, such as Spark Streaming, MLlib, and GraphX, for real-time data processing requirements. Moreover, I actively incorporate DevOps practices into my workflow to ensure seamless collaboration between development and operations teams. This includes the integration of continuous integration/continuous deployment (CI/CD) pipelines, automated testing, and infrastructure as code (IaC) principles. Embracing a DevOps mindset enhances the overall efficiency and reliability of the software development lifecycle. I take pride in my ability to align machine learning methodologies with data processing workflows to meet client demands effectively. This involves leveraging Spark MLlib for predictive modeling and classification tasks, ensuring a holistic approach to addressing client requirements and business objectives. Throughout my journey in data science, I have remained dedicated to staying at the forefront of technology, constantly adapting to new tools and methodologies. I am enthusiastic about bringing this multifaceted expertise, encompassing data science and DevOps practices, to tackle new challenges and make meaningful contributions to future projects.

If your data team spends more time fixing pipelines than reading insights, that's not a people problem — it's an infrastructure problem. I'm a Top Rated Analytics Engineer with 6+ years building data infrastructure that operations teams actually rely on. I've worked across fintech (Zeta), agri-tech (Nurture.Farm / UPL), and growth-stage startups — designing warehouse architectures, building dbt pipelines, and creating dashboards that drive real decisions. Two results that tell you what I actually do: ⦁ Reduced a critical Redshift model runtime from 8 minutes to 40 seconds by redesigning sort keys, unique keys, and materialization strategy — unblocking daily reporting for an entire product team. ⦁ Cut machine breakdown response time from 48 hours to 24 hours for 93% of a 500+ machine fleet through real-time alerting dashboards connected to live operational data. What I build for clients: ⦁ Cloud data warehouses on Redshift and BigQuery — structured in dbt with full lineage, tests, and documentation ⦁ Airflow-orchestrated pipelines that run on schedule and alert you when they don't ⦁ Tableau, Power BI, Looker Studio, and Superset dashboards built around the decision, not the data ⦁ Data quality frameworks with row-level validation and automated monitoring ⦁ Real-time analytics pipelines using AWS Athena, Python automation, and SQL I work best with founders, data leads, and product teams who want analytics to be a competitive advantage, not a maintenance burden. I document everything, communicate proactively, and deliver what I said I would — when I said I would. If you have a data pipeline, warehouse, or dashboard problem — send me a message. I'll tell you within 24 hours whether I can fix it and exactly how.

I am a skilled Data Engineer and Python Developer with hands-on experience in designing, building, and optimizing scalable data pipelines, databases, and AI-powered applications. I specialize in transforming raw data into reliable, high-quality datasets that support analytics, reporting, and intelligent decision-making. My expertise includes developing ETL/ELT pipelines, writing optimized SQL queries, managing cloud data warehouses, and administering Snowflake environments. I have experience with database design, performance tuning, data modeling, and ensuring data security and reliability across modern data platforms. In addition to data engineering, I develop Python-based automation tools, backend services, and AI-powered solutions using Generative AI and AI agents. I enjoy building intelligent applications that automate workflows, improve productivity, and solve real-world business challenges. Core Expertise: Data Engineering & ETL/ELT Pipeline Development Snowflake Administration & Performance Optimization Database Administration (SQL & NoSQL) Advanced SQL Query Development & Performance Tuning Python Development & Automation AI Agent Development & Generative AI Applications Data Warehousing & Data Modeling AWS Services & Cloud-Based Data Solutions MongoDB & Database Management Node.js Backend Development React Web Application Development Microsoft Power BI Dashboards & Data Visualization I am committed to writing clean, maintainable, and efficient code while delivering reliable, scalable, and business-focused solutions. Whether you need a robust data pipeline, Snowflake administration, database optimization, Python automation, or AI-driven applications, I can help deliver high-quality results on time. Let's work together to build secure, scalable, and intelligent data solutions that drive your business forward. #DataEngineer #PythonDeveloper #Snowflake #SnowflakeAdministrator #SnowflakeDeveloper #DatabaseAdministrator #SQL #AdvancedSQL #SQLDeveloper #DataEngineering #DataWarehouse #DataWarehousing #ETL #ELT #DataPipelines #DataPipelineDevelopment #DataModeling #DataArchitecture #DatabaseManagement #DatabaseDesign #DatabaseOptimization #DatabasePerformance #QueryOptimization #PerformanceTuning #DataMigration #DataIntegration #DataTransformation #DataProcessing #DataCleaning #DataValidation #DataGovernance #DataQuality #DataAnalysis #DataAnalytics #BusinessIntelligence #PowerBI #DashboardDevelopment #DataVisualization #Python #PythonProgramming #PythonAutomation #PythonScripting #BackendDevelopment #NodeJS #ExpressJS #ReactJS #JavaScript #WebDevelopment #WebApplicationDevelopment #RESTAPI #APIDevelopment #Microservices #MongoDB #NoSQL #AWS #AWSCodeBuild #CloudComputing #CloudDataEngineering #CloudArchitecture #AmazonWebServices #GenerativeAI #ArtificialIntelligence #AI #AIAgent #AIAgentDevelopment #AgenticAI #LLM #LargeLanguageModels #PromptEngineering #RAG #VectorDatabase #Embeddings #MachineLearning #DeepLearning #NaturalLanguageProcessing #NLP #Automation #WorkflowAutomation #DataScience #BigData #AnalyticsEngineering #DataOps #DevOps #Git #GitHub #Docker #Linux #Ubuntu #JSON #CSV #APIIntegration #ScalableApplications #SoftwareDevelopment #SystemDesign #CloudSolutions #BusinessAnalytics #Reporting #DataSecurity #DataCompliance #RealTimeData #DataLake #DataLakehouse #Snowpark #DataEngineer #PythonDeveloper #SnowflakeAdministrator #SnowflakeDeveloper #SnowflakeArchitect #SnowflakeSQL #Snowpark #DatabaseAdministrator #DatabaseDeveloper #DatabaseEngineer #SQLExpert #SQLProgramming #TSQL #PLSQL #DataEngineering #CloudDataEngineer #BigDataEngineer #AnalyticsEngineer #DataArchitect #DataWarehouseDeveloper #DataWarehouseArchitect #DataWarehouseAutomation #DataLake #DataLakehouse #LakehouseArchitecture #ETLDeveloper #ELTDeveloper #ETLPipelines #DataPipelines #DataOrchestration #ApacheAirflow #ApacheSpark #PySpark #SparkSQL #Databricks #AWS #AWSDeveloper #AWSCloud #AWSDataEngineering #AWSCodeBuild #AmazonS3 #AmazonEC2 #AWSLambda #CloudInfrastructure #CloudMigration #CloudSolutions #CloudAutomation #Python #PythonExpert #PythonAutomation #PythonScripting #PythonProgramming #Pandas #NumPy #JupyterNotebook #AutomationScripts #BackendDeveloper #NodeJS #ExpressJS #RESTAPI #APIIntegration #FastAPI #Flask #ReactJS #JavaScript #TypeScript #HTML5 #CSS3 #TailwindCSS #Bootstrap #MongoDB #NoSQL #MySQL #PostgreSQL #OracleSQL #MicrosoftSQLServer #DatabaseSecurity #DataEncryption #Indexing #QueryOptimization #PerformanceOptimization #DatabaseBackup #DataRecovery #DataGovernance #MasterDataManagement #DataQuality #DataValidation #BusinessIntelligence #MicrosoftPowerBI #PowerQuery #DAX #DashboardDevelopment #DataVisualization #GenerativeAI #OpenAI #LLM #PromptEngineering #AgenticAI #AIAgents #MachineLearning #ArtificialIntelligence #DeepLearning #NLP #RAG #VectorSearch #Embeddings #SemanticSearch #MCP