Seasoned data engineer with over 11 years of experience in building sophisticated and reliable ETL applications using Big Data and cloud stacks (Azure and AWS). TOP RATED PLUS . Collaborated with over 20 clients, accumulating more than 2000 hours on Upwork. 🏆 Expert in creating robust, scalable and cost-effective solutions using Big Data technologies for past 9 years. 🏆 The main areas of expertise are: 📍 Big data - Apache Spark, Spark Streaming, Hadoop, Kafka, Kafka Streams, Trino, HDFS, Hive, Solr, Airflow, Sqoop, NiFi, Flink 📍 AWS Cloud Services - AWS S3, AWS EC2, AWS Glue, AWS RedShift, AWS SQS, AWS RDS, AWS EMR 📍 Azure Cloud Services - Azure Data Factory, Azure Databricks, Azure HDInsights, Azure SQL 📍 Google Cloud Services - GCP DataProc 📍 Search Engine - Apache Solr 📍 NoSQL - HBase, Cassandra, MongoDB 📍 Platform - Data Warehousing, Data lake 📍 Visualization - Power BI 📍 Distributions - Cloudera 📍 DevOps - Jenkins 📍 Accelerators - Data Quality, Data Curation, Data Catalog

Hi, I’m Uzair—a seasoned data architect specializing in architecting cutting-edge data products using the Databricks platform. With over 7 years of experience and 50+ successful projects under my belt, I’ve mastered the art of transforming complex data challenges into scalable, AI-powered solutions. My expertise centers on leveraging the Databricks Lakehouse Platform to build robust data pipelines, seamlessly integrate machine learning models, and drive actionable insights across multiple industries. My Expertise in Databricks-Centric Data Product Architecture Databricks & Big Data: I design and implement large-scale data processing solutions using Databricks’ Apache Spark engine. By architecting efficient pipelines and utilizing Delta Lake, I ensure that your data infrastructure is both resilient and agile, ready to power real-time analytics and business intelligence. Machine Learning & Deep Learning on Databricks: I develop and deploy scalable machine learning models directly within the Databricks environment. Leveraging frameworks like TensorFlow, PyTorch, and scikit-learn alongside MLflow for model tracking, I help businesses unlock predictive capabilities and automate complex decision-making processes. Cloud Engineering with Databricks: Whether on AWS, Azure, or GCP, I architect and optimize Databricks clusters that integrate seamlessly into your cloud infrastructure. My solutions ensure that your data products are both robust and scalable, offering a unified platform for data engineering, analytics, and AI. Data Analytics & Visualization: Using Databricks notebooks and SQL analytics, I transform raw data into actionable insights. I further integrate these insights with visualization tools like Tableau and Power BI, crafting intuitive dashboards that drive strategic business decisions. End-to-End Data Product Development: From data ingestion to model deployment and continuous improvement, I design end-to-end data products that harness the full power of the Databricks ecosystem. My approach is tailored to meet each client’s unique needs, ensuring efficient, scalable, and cost-effective solutions. Through expert data analysis, streamlined machine learning pipelines, and advanced infrastructure design on Databricks, I help organizations modernize their data architecture—unlocking the full transformative potential of AI and data. Let’s collaborate to build innovative data products that give your business a competitive edge.

A working demo and a system that survives production are two very different things. I build the second kind. Over a decade of backend engineering and 70+ completed projects on Upwork. I run Datum Brain, a software and AI engineering company, so you get one senior engineer accountable end to end, with a team behind him when the work needs it. We ship Go microservices, data pipelines, and LLM systems for FinTech, SaaS, EdTech, and AdTech clients. Recent work: 🛡 Real-time ad fraud detection (TrueAudience) High-concurrency Go backend that scores ad traffic to catch bots before advertisers pay. 80M+ events, under 20ms decisions. Go, TimescaleDB, real-time pipelines. 🏦 Go microservices for an EMI and payments platform (Zolvat) Ledger logic, payment flows, and service orchestration for an installment payments platform. Go, microservices, PostgreSQL. 🎥 WebRTC proctoring infrastructure (Meazure / Examity) Real-time media and session orchestration for thousands of concurrent exam sessions at one of the largest US proctoring providers. Go, WebRTC, LiveKit, NATS. 📊 Big data pipeline optimization (Nike) Cut processing time and compute cost on large PySpark workloads. PySpark, AWS. 📄 Document intelligence platform (Centrum AI) OCR and RAG pipeline that turns unstructured documents into structured, queryable data with human review. Python, FastAPI, LangChain, RAG. What I work with: Backend: Go, Python, FastAPI, gRPC, REST, PostgreSQL, TimescaleDB, Redis, NATS, Kafka Data: PySpark, ETL pipelines, data quality frameworks, AWS, GCP Real-time: WebRTC, LiveKit, event streaming, high-concurrency systems AI and LLM: LangChain, LangGraph, RAG, multi-agent systems, OpenAI, Claude, evals Infra: Docker, Kubernetes, CI/CD, nginx How I work: I scope before I quote, so you know what you are getting and when. I flag risks early instead of at the deadline. Production-ready means tested, documented, monitored, and handed over properly. Good fit if you need something that runs in production and not a proof of concept, you want one senior engineer accountable end to end, and you value clear communication and honest scoping. Not a fit if you are shopping purely on hourly rate, or the spec changes daily and nobody owns decisions. If your backend needs to handle real load, or your data or AI work needs to go from demo to product, send me a message.

𝗚𝗿𝗲𝗮𝘁 𝗔𝗜 𝗦𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗚𝗿𝗲𝗮𝘁 𝗗𝗮𝘁𝗮. The success of any AI application depends on far more than selecting the right language model. Reliable AI requires high-quality data pipelines, scalable infrastructure, intelligent retrieval, and production-ready architecture that delivers accurate results under real-world conditions. I help organizations build the data foundations behind modern AI products. My expertise combines Data Engineering, Retrieval-Augmented Generation (RAG), Machine Learning, Vector Search, and AI Infrastructure to transform fragmented business data into intelligent, searchable, and scalable systems. From enterprise knowledge platforms and semantic search to ETL pipelines, MLOps, document intelligence, and AI infrastructure, I design solutions that support long-term growth while maintaining performance, reliability, and operational efficiency. 𝗖𝗼𝗿𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 • Retrieval-Augmented Generation (RAG) • Enterprise Search • Semantic Search • AI Knowledge Bases • Data Engineering Pipelines • ETL / ELT Development • Machine Learning Infrastructure • MLOps & Model Deployment • AI Analytics Platforms • Intelligent Document Processing • Recommendation Systems • AI Infrastructure Consulting 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 Data Engineering • Python • SQL • Apache Spark • Apache Airflow • Kafka AI Infrastructure • LangChain • LlamaIndex • Vector Databases • AI Evaluation • MLOps Vector Search • Pinecone • Weaviate • Qdrant • Elasticsearch • pgvector Cloud • AWS • Azure • Google Cloud • Docker • Kubernetes Industries • Healthcare • Financial Services • SaaS • HR Tech • Logistics • Manufacturing • Retail • Education • LegalTech I believe AI should be built on reliable engineering foundations. Every project is designed around scalable architecture, clean data pipelines, retrieval accuracy, security, observability, and long-term maintainability. My goal is to help businesses create AI platforms that remain reliable as data, users, and business requirements continue to grow.

Senior Data Engineer and AI Engineer with 15+ years in data engineering, AI engineering, and cloud data platforms. I build production data pipelines, ETL workflows, LLM and RAG systems, and machine learning infrastructure on AWS, GCP, Azure, Snowflake, Databricks, and Apache Spark. Most of my data engineering work is end to end. I take a messy data problem, or an AI feature that "almost works," and turn it into something reliable that runs in production without someone babysitting it. After 15+ years as a data engineer, you learn the hard part is rarely the model or the framework. It's the data, the edge cases, the pipelines, and keeping the system maintainable once you've handed it over. As an AI engineer I treat LLM and RAG work the same way: take a prototype that almost holds together and make it a production AI system that survives real traffic, real users, and real data. Recent data engineering and AI engineering projects: - An AI inbound phone system built on Twilio with OpenAI Whisper and GPT-4, handling real-time voice intake and call routing. - Enterprise Power BI data models for healthcare and financial reporting, around 30 tables and 40+ DAX measures, including IFRS 9 staging, RAROC, and NIM trends. - HL7 FHIR R4 integrations with Epic, Cerner, and Athenahealth for a clinical AI platform. - Cut LLM inference cost on a high-volume voice product by 25% by reworking how it used the OpenAI Realtime API and its per-turn token replay. Data engineering: Apache Spark, PySpark, dbt, Apache Airflow, ETL and ELT pipelines, data warehousing, data modeling, Snowflake, BigQuery, Redshift, Databricks, Delta Lake, Kafka, Kinesis, Fivetran. AI engineering and machine learning: OpenAI GPT-4o, Claude, Gemini, LangChain, LlamaIndex, RAG pipelines, AI agents, prompt engineering, vector search (Pinecone, Weaviate, pgvector), PyTorch, TensorFlow, scikit-learn, MLflow, model deployment. Cloud and DevOps: AWS (Glue, EMR, Lambda, Redshift, SageMaker, Athena), GCP (BigQuery, Dataflow, Vertex AI), Azure (Synapse, Data Factory, Azure ML), Terraform, Docker, Kubernetes, GitHub Actions. Automation and integration: n8n, Make, Power Automate, REST and GraphQL APIs. Governance and compliance: GDPR, HIPAA, SOC 2, RBAC, PII masking, encryption, data lineage. Languages: Python, SQL, Scala, PySpark, FastAPI, Flask. How I work: I'd rather ask the right questions up front than build the wrong thing quickly. I'll tell you when something is a bad idea, give you timelines I can keep, and leave you with code and documentation your own team can maintain. I've delivered data engineering and AI engineering projects for startups and enterprises across the US, Europe, and Asia. If you're looking to hire a data engineer or AI engineer who can own the work end to end, from raw data pipeline to production AI system, I'm available now. Tell me what you're building and I'll give you a straight answer on how I'd approach it.

I'm a certified AWS DevOps engineer with over a decade in the field. I specialize in managing and analyzing Linux/Unix enterprise servers, handling SaaS applications, containers, and various web languages through APIs, all while focusing on Docker, Kubernetes, and Terraform; also I have experience working with ML, MLOps and AI. My web language experience relies on Node.js, React, Python, Java Spring Boot, and .NET, all integrated via APIs. My expertise lies in DevOps, continuous integration, and microservices, where I leverage Kubernetes, Terraform, and CI/CD pipelines to enhance efficiency. Certifications: AWS Certified Solutions Architect – Associate AWS Certified Security – Specialty Azure Fundamentals Azure DevOps Engineer – Expert Azure AI – Fundamentals Azure Security – Fundamentals Superlative experience on: Amazon Web Services (AWS): AMAZON EC2, Amazon CloudFront, CloudFormation, S3, Amazon RDS, Amazon ELB, Amazon Auto Scaling, Amazon VPC, Route53, Amazon ECS, Fargate, Beanstalk, Lambda, AWS Code Deploy, etc. Serverless developer: API Gateway, Amazon cognito, Amazon Lambda, DynamoDB, and more. Azure: These past years I've experimented over Microsoft Azure Cloud Provider, providing support and infrastructure resources using services as Virtual Machines, Storage Accounts, SQL Flexible servers for MySQL, PostgreSQL and Microsoft SQL, App Services, Cloud Services, Function Apps, Application Gateways, DNS Zone, Container Registries, Container Instances and Kubernetes, provided manually and using ARM Templates or Terraform for IAC. Finally, I've worked on providing user management best practices using Microsoft Entra ID and Azure RBAC. DevOps:I'm dedicated to automating Infrastructure as Code, using a variety of tools to streamline my workflows. My toolkit for building and managing multitenant infrastructures includes Terraform, Docker, AWS ECS, Kubernetes, Ansible, Chef, AWS CloudFormation, and AWS Vagrant. In the realm of CI/CD, I rely on a comprehensive set of tools such as Jenkins, CircleCI, TravisCI, AWS CodeDeploy, AWS CodeCommit, AWS ECS, Bitbucket Pipelines, GitLab, and Webhooks. DevSecOps: I focus on securing digital infrastructures efficiently. I use tools like Snyk, SonarQube, Sonatype, JFrog, and Nexus for code analysis and vulnerability assessments. These tools are integrated into GitLab workflows for automated security checks. I also implement SOC2 practices to ensure robust security and compliance. Compliances: HIPAA, PCI & SOC2 Big Data and Analytics:Amazon RedShift, Airflow, AWS Glue, AWS EMR, AWS sagemaker, tensorflow, AWS Kinesis, Hadoop, Apache Spark, and Cassandra.

Your leads are waiting. Someone filled out your form an hour ago, and they're already talking to a competitor. I build AI agents and automations that answer, qualify, and book every lead in minutes, 24/7. Maybe your bottleneck isn't leads. Maybe it's support tickets piling up, data copied between systems by hand, or a report someone rebuilds every Monday. Same fix: an AI agent or automation that does the work reliably, with guardrails and human handoff where it matters. What you get: - Speed-to-lead in minutes, not hours: AI agents that respond, qualify, follow up, and book from your forms, email, or WhatsApp - A chatbot that actually knows your business: RAG, trained on your docs and CRM (Claude / OpenAI) - Workflows that run without you: n8n, Make, or custom Python connecting CRM, email, sheets, and your data warehouse - Clean data underneath: Azure / Databricks / Snowflake pipelines, because AI built on messy data fails Why trust me with it: - I run an AI sales agent platform with paying B2B customers. The systems I'd build for you are answering and qualifying real leads for real businesses today, including mine - Years as an enterprise data architect and consultant, so the data layer gets built right instead of skipped - Top Rated Plus · 100% Job Success · 2,400+ hours on Upwork - Fixed scopes priced on outcomes (speed-to-lead, conversion, hours saved), not open-ended hourly drift Tell me what's eating your team's hours, or book a consultation. You'll get a concrete approach, timeline, and fixed price within 24 hours.

𝗧𝗼𝗽 𝗥𝗮𝘁𝗲𝗱 𝗣𝗹𝘂𝘀 | 𝟭𝟬𝟬% 𝗝𝗼𝗯 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 | $𝟭𝟬𝟬𝗞+ 𝗘𝗮𝗿𝗻𝗲𝗱 𝗼𝗻 𝗨𝗽𝘄𝗼𝗿𝗸 I help businesses turn messy data and manual workflows into clean, automated, AI-powered systems. With 7+ years of engineering experience focused on Claude AI, document intelligence, and production-grade data pipelines, I build infrastructure that lets companies scale without scaling headcount. 𝗪𝗵𝗮𝘁 𝗜 𝗕𝘂𝗶𝗹𝗱 𝗖𝗹𝗮𝘂𝗱𝗲 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Anthropic Claude API integrations - Multi-step AI agents & agentic workflows - RAG systems with LangChain & vector databases - Prompt engineering, tool use, function calling 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 - OCR + LLM extraction from contracts, forms, receipts, scanned PDFs - Document classification & hierarchical taxonomies - Layout analysis & structured data extraction - On-premise RAG over PDF collections 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 & 𝗘𝗧𝗟 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 - Apache Airflow orchestration at 350+ scraper scale - Spark, PySpark, Kafka for streaming & batch - Snowflake, Databricks, AWS Glue, dbt - Multi-tenant ETL with 95%+ success rates 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 - n8n, Make, Zapier for end-to-end workflow automation - LLM-powered chatbots (Shopify, Amazon SP-API, HubSpot) - Webhook orchestration & REST API integration - Custom Python automation for repetitive operations 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 - AWS Lambda, S3, Glue, EC2, Bedrock, Step Functions, ECS - CI/CD pipelines with automated testing & rollback - Docker & Kubernetes for containerized services - HIPAA, SOC 2, GDPR-compliant deployments 𝗥𝗲𝗰𝗲𝗻𝘁 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 - $1.2M+ business impact through AI-driven automation - 350+ daily scrapers via Apache Airflow with 95%+ success rate - 24/7 automated Shopify + Amazon refund chatbot via n8n + LLM - 80% reduction in manual calls via NLP voice agent - 95%+ data accuracy across HIPAA-compliant healthcare pipelines 𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸 Clear communication, realistic timelines, clean code, and finished products that work in production. Most clients come back; many become long-term partners. Got a slow workflow, a pile of documents that needs structure, or an AI idea you want shipped? Send a quick message and I'll respond within a few hours. Let's connect.

I am Data Architect/Snr. Data Engineer with 12 years experience with RDBMS/NoSQL databases and processing large amounts of data. My experience related to enterprise level and high profile projects in the past, but now I'm helping startups and small-mid sized companies to achieve their goals! My core competences are: Data Modelling, Data Architecture on Cloud platforms, Database development, ETL and Business Intelligence, Database Administration Solution Architecture : Design of solution architectures for a data processing systems of various scale and purpose. Definition of up-to-date technical solutions including data storage, network, data processing (ELT/ETL), BI and AI/ML components. Process and methods definitions and optimizations. AI/ML : Amazon AI Services, LLM Integration (OpenAI, Gemini), AWS Bedrock, AWS Sagemaker, pgvector, Databricks Mozaic Data Modelling : Modelling of OLTP and Datawarehouse systems. It could be design of new schema, normalization/denormalization of existing model, Enterprise datawarehouse design based on Kimball/Inmon, Data Lake and Data Vault architectures, Modernization of existing data landscape. Data Lakes : Modern datalakes built on cloud storages as GCS/S3/Azure Datalake Gen2 with Databricks, AWS Glue, Trino, Apache Iceberg DBA Activities : DB migrations, Backup & Recovery, Upgrades, Instance configurations, DB Monitoring, Horizontal scaling, Streaming/BDR replications. Sharding with postgreSQL extensions. Data Integration and ETL : Traditional batch ETL - Informatica, Talend, AWS Datapipeline, Matillion ETL Serverless ETL - AWS Lambda, AWS Glue, Batch, AWS DMS, Google Cloud Functions, Databricks Streaming ETL - Apache NiFi, Kafka, Kinesis streams SaaS ETL - Stitch, Alooma, Fivetran, Airbyte BI/data layers - dbt/Prefect Direct loading with DBMS tools & scripting Data Governance and MDM: Design and implementation of solutions based on Informatica DG/MDM, Alation, Atlan, custom dataquality solutions. BI Systems : Design of BI systems and implementation. I had experience with major industry leading tools as Tableau, PBI, Looker and cloud alternatives. Additionally i had experience with old-style reporting solutions from SAP, Qlick, Jasper. Cloud containerization and deployment : Docker, Mesos/Kubernetes Java development : EE/SE , Spring, Hibernate, RESTful APIs, Maven Clouds : - Cloud migrations (AWS, Azure, GCP) - Cloud infrastructures (VPCs, EC2, Loadbalancing, Autoscaling, Security in AWS/GCP) Please note that minimum engagement is part-time (20 hrs/week) and month long, this will ensure the quality of delivered solution and mutual benefit. Thank you for getting to the end of this boring details and looking forward working on exciting projects together :) Best Regards, Yegor.

I help enterprises move AI from pilot to production and with solid data infrastructure. 20+ years building systems that survive real users and compliance requirements (SOC 2, HIPAA, GDPR). I have a strong background and experience working with companies like DXC, Lincoln National, Jackson National, and Cisco. Most "AI consultants" can demo a chatbot. Fewer can ship an agentic system that talks to your existing databases, respects your security posture, and doesn't fall over when the API rate-limits you at 2am. That's the gap I work in. What I actually build: AI agents & automation: Multi-agent systems using LangChain, LangGraph, CrewAI, and MCP-based orchestration. RAG pipelines with vector databases (Pinecone, Weaviate, pgvector) that actually retrieve the right thing. Voice AI with Twilio and Vapi for intake and support workflows. Workflow automation connecting AI to business systems via n8n, Zapier, and custom Python. I've built pipelines that take raw scraped or unstructured data and turn it into structured reports with zero manual touch. Data engineering: ETL/ELT pipelines on Airflow, dbt, and Spark; warehouses on Snowflake, BigQuery, Databricks, and the major clouds (AWS, Azure, GCP); real-time streaming with Kafka/Kinesis. If your AI project is stalling because the data underneath it is a mess, this is usually where I start. MLOps & production infrastructure: CI/CD for ML, model monitoring and drift detection, Docker/Terraform-based deployment. I build for handoff, not just demo day — clear documentation, monitored pipelines, and systems your internal team can actually maintain after I'm gone. Compliance-aware builds: Experience delivering under CMMI Level 5, SOC 2, HIPAA, and GDPR constraints, which matters if you're in finance, healthcare, or insurance (where most of my enterprise work has been). I work best with clients who have a real business problem, not just "we want to use AI somewhere. Tell me what's broken or slow, and I'll tell you honestly whether AI/automation is the right fix and if it's not, I'll say so before you spend the budget. Available now for both project-based work and ongoing engagements. Invite me to your job post to discuss your data infrastructure needs. AI Agents | Agentic Workflows | Document Intelligence | ERP Automation | RFQ Automation | Claude AI | OpenAI | Manufacturing AI | Industrial Automation Supply Chain Intelligence | Predictive Analytics | Inventory Optimization | Snowflake | Data Engineering | Manufacturing Intelligence | AI Recommendations AI Compliance Copilot | RAG | Enterprise Search | Knowledge Management | Document Intelligence | AI Governance | Regulatory Compliance Enterprise Data Platform | Data Engineering | Snowflake Migration | MDM | Entity Resolution | Executive Analytics | Post-Merger Integration Engineering Copilot | RAG | Semantic Search | Proposal Automation | AI Estimation | Knowledge Intelligence | Generative AI CFO Copilot | Financial Intelligence | Snowflake | Executive Dashboards | Variance Analysis | AI Reporting | Data Modernization - AI Agents - Agentic Workflows - AI Automation - Enterprise AI - AI Integration - RAG (Retrieval-Augmented Generation) - Multi-Agent Systems - LLM Applications - AI Copilots - Workflow Automation - Knowledge Management - Document Intelligence - ERP Automation - Snowflake - dbt - Airflow - Data Engineering - Analytics Engineering - Vector Databases - LangGraph - n8n - MCP (Model Context Protocol) - AI-Powered Decision Support - AI Operations (AIOps) - Predictive Analytics - Intelligent Document Processing (IDP) - Process Mining - Digital Transformation - Manufacturing Intelligence - Supply Chain Optimization - AI-Powered Compliance - Generative AI Solutions - Enterprise Search - Semantic Search - Conversational Analytics - Executive AI Dashboards - AI Governance - Human-in-the-Loop AI - Autonomous Business Workflows