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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.

I build reliable, production-grade data pipelines on AWS for teams that need accurate data, predictable costs, and systems that scale without breaking. I’m an AWS data engineer with 6 years of experience designing and running ETL/ELT pipelines, data lakes, and analytics platforms used for decision-making and compliance. If your pipelines fail silently, dashboards can’t be trusted, or costs keep rising, the issue is usually the architecture — not the tools. I work with: S3, Glue, Lambda, Step Functions Redshift, Athena, Snowflake (AWS) Kinesis, MSK / Kafka Airflow What you get: Clean, well-documented data models Monitored pipelines (no silent failures) Cost-efficient, scalable AWS designs Systems your team can operate without me If you’re struggling with broken pipelines, slow analytics, or rising AWS data costs, message me. I’ll tell you quickly if I can help and what I’d do first.

I am a Senior Healthcare Software Architect with expertise in building HIPAA compliant secure, scalable, and compliant digital health solutions. Navigating the intersection of healthcare and technology requires more than just coding skills, it requires a deep understanding of HIPAA compliance, data security, and clinical workflows. I specialize in developing end-to-end healthcare ecosystems that empower providers and improve patient outcomes. With a focus on security-first development, I help healthcare startups and established practices build robust digital tools that meet the highest regulatory standards. 🛠 Core Services & Expertise: HIPAA-Compliant Portals: Custom-built Patient and Practitioner portals with secure messaging, scheduling, and billing. EHR/EMR Integrations: Seamless interoperability with major systems (Epic, Cerner, Allscripts) using HL7 and FHIR standards. Remote Patient Monitoring (RPM): Building solutions that track real-time patient data and integrate directly into provider dashboards. Telehealth Solutions: Secure video consultations and asynchronous care platforms. Healthcare Data Security: Implementation of AES-256 encryption, MFA, and comprehensive audit logs. 🚀 Why Work With Me? Compliance by Design: I don’t just "add security" at the end; I build compliance into the architecture from day one. User-Centric Workflows: I understand the unique needs of clinicians (reducing burnout) and patients (improving engagement). Scalable Architecture: My solutions are built to grow with your practice or user base without compromising performance.

🥇 𝐄𝐱𝐩𝐞𝐫𝐭-𝐕𝐞𝐭𝐭𝐞𝐝 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 | 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐀𝐈, 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧, 𝐆𝐞𝐧𝐀𝐈, 𝐋𝐋𝐌𝐎𝐩𝐬, 𝐌𝐋𝐎𝐩𝐬 I turn healthcare AI prototypes into HIPAA-compliant, FDA-ready production systems on AWS/GCP/Azure. No compliance violations. No runaway cloud bills. No PHI leaks. 💼 What I Build ✔ 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐆𝐞𝐧𝐀𝐈 & 𝐀𝐠𝐞𝐧𝐭𝐬 – Multi-agent systems (AWS Bedrock AgentCore, LangGraph, AutoGen) for prior authorization, clinical trial matching, claims processing. Agentic RAG with FHIR/EHR grounding + human-in-the-loop validation. ✔ 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 – Radiology (X-ray, CT, MRI), pathology, retinal imaging, surgical video. YOLO v8-v10, Vision Transformers (ViT), SAM, MONAI, nnU-Net, UNETR. Segmentation, detection, 3D reconstruction. ✔ 𝐇𝐈𝐏𝐀𝐀/𝐌𝐋𝐎𝐩𝐬 – Promptfoo CI/CD blocks prompt injections, jailbreaks, PHI leaks. Langfuse observability. Full audit trails for FDA 510(k). ✔ 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 – FHIR, DICOM, HL7, X12 EDI. EMR integration (Epic, Cerner). Vector DBs: Pinecone, Weaviate, pgvector. ✔ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐈 – Risk stratification, readmission, sepsis detection. Clinical NLP (ICD-10, CPT, SNOMED CT, LOINC, RxNorm). Time series for vitals + wearables. 📈 Proven Business Impact ✔ 98% document classification accuracy → 8.5min to 1.5min (77% cost cut) ✔ 90% auto-extraction for prior authorization → $132K annual savings ✔ 23% no-show reduction → $3.5M recovered revenue ✔ 23% collections uplift via LLM payer negotiation ✔ 11+ hours to 36 minutes pathology workflow reduction ✔ 134PB medical imaging across 34M patient exams 🛠️ The Enterprise Tech Stack ✔ Healthcare Cloud – AWS HealthLake, Comprehend Medical, Bedrock (HIPAA) | GCP Healthcare API | Azure Health Data Services ✔ Medical CV – MONAI, nnU-Net, UNETR, MedSAM, YOLO, ViT, SAM, CLIP, PyTorch, TensorRT, OpenCV ✔ GenAI/LLM – Claude, GPT-4o, Gemini, Med-PaLM 2, BioBERT, ClinicalBERT, LangGraph, LlamaIndex, DSPy, PydanticAI, Guardrails, LangChain, Vertex AI, SageMaker ✔ LLMOps – Promptfoo, Langfuse, Arize Phoenix, W&B, MLflow, RAGAS, DeepEval ✔ Vector DB – Pinecone, Weaviate, Qdrant, Milvus, pgvector, Neo4j ✔ MLOps – Terraform, Kubernetes, Docker, Lambda, ArgoCD, Helm, Kubeflow, Airflow, dbt ✔ Interoperability – FHIR, DICOMweb, HL7v2, IHE, SMART on FHIR, CDS Hooks, X12 EDI, NCPDP ✔ Standards – ICD-10, CPT, SNOMED CT, LOINC, RxNorm, RadLex, OMOP CDM, i2b2 ✔ Monitoring – Prometheus, Grafana, Datadog, OpenTelemetry ✔ Compliance – HIPAA, FDA SaMD (510k, De Novo), 21 CFR Part 11, GxP, GDPR, HITRUST, SOC 2, ISO 27001, NIST ✔ FullStack – Next.js, React, TypeScript, Node.js, Python, FastAPI, Express.js, PostgreSQL, MongoDB, Supabase, Firebase, SaaS ✔ Mobile – React Native, Expo, Android (Kotlin), iOS (Swift, SwiftUI), Firebase, Push Notifications, App Store / Play Store deployment 🚀 How We Work ✔ Audit → Cloud footprint, PHI flows, compliance gaps, model drift, FinOps ✔ Blueprint → Terraform + HIPAA controls + FDA documentation templates ✔ Deploy → Promptfoo + Langfuse + Vanta. Blocks injections, jailbreaks, PHI leaks ✔ Validate → Sensitivity/specificity, F1, AUC, bias audits, clinician-in-loop ✔ Handoff → Stabilized budget + team training + runbooks 📩 Invite me to your healthcare AI job. I'll reply within 2 hours with one specific question about your pipeline or compliance gap.

🎯 Results my clients achieve: → 60% reduction in data processing costs through optimized AWS Lambda and S3 implementations → Automated reporting systems saving 15+ hours weekly across teams → Seamless integration of multiple data sources into unified, reliable data warehouses → Enhanced data quality and governance with proven medallion architecture I specialize in building robust, scalable data solutions that transform business operations. My expertise spans: 🔄 Data Pipeline Engineering: - AWS Lambda-powered data pipelines processing 1GB+ daily data with medallion architecture - Multi-source ETL systems handling PostgreSQL, Snowflake, and BigQuery migrations - Healthcare data integration across 10+ states with automated PDF/Excel parsing - Large-scale YouTube analytics pipeline with geospatial processing - Real estate analytics system with automated data quality checks - Anime seasonal data collection pipeline using MyAnimeList API - Oil price prediction pipeline on AWS EC2 (r5.2xlarge) with PySpark 🛠️ Tech Stack Expertise: Cloud & Infrastructure: - AWS: Lambda, S3, CloudWatch, EC2, CLI - GCP: BigQuery, Cloud Storage - Infrastructure: Docker, Git, CI/CD Data Processing & Storage: - Frameworks: PySpark, Apache Airflow, Pandas, PyArrow - Databases: PostgreSQL, Snowflake, BigQuery, MySQL - Formats: Parquet, CSV, Hive partitioning, XML Analytics & Visualization: - BI Tools: Metabase, Apache Superset, Cluvio, Looker Studio - Libraries: Matplotlib, Seaborn, GeoPandas - Platforms: Amplitude, Google Analytics 4, Chartio AI & ML Integration: - LLM Implementation: Llama2, Langchain, OpenAI - ML Libraries: Scikit-learn, Facebook Prophet, XGBoost - Vector Stores: Pinecone, Hugging Face Embeddings Specializations: - Web Scraping: BeautifulSoup, Selenium, Custom Extractors - API Integration: RESTful APIs, OAuth 2.0, Webhooks - Data Quality: DBT transformations, Custom validation frameworks 🎓 Education & Certifications: - AWS Certified Data Engineer - Associate - Databricks Certified Data Engineer Associate - MS in Business Consulting (Germany) - MS in Big Data Analytics & AI (Russia) 💡 What sets me apart: 1. Proven track record with $30K+ earnings and 55+ successful projects 2. Perfect 5.0 client satisfaction rating 3. Experience across diverse industries (Real Estate, Healthcare, Legal) 4. Strong focus on cost optimization and efficiency 5. Clear communication and detailed documentation Ready to transform your data challenges into opportunities? Let's discuss how I can help optimize your data infrastructure and deliver actionable insights for your business. Let's turn our discussion into action. I can start immediately and deliver the quality results you're looking for.

Most healthcare app developers can build a form. Very few can build a HIPAA-compliant, AI-powered mental health platform that handles patient intake via voice, runs therapy session tracking, monitors mood in real time, and escalates crisis alerts — all from a single codebase deployed on Azure. I've done exactly that. Working with a US-based surgeon, I architected and delivered a full-stack mental health platform from the ground up — voice AI intake via Twilio IVR, AI chatbot with mood tracking, therapy session management, medical scribe (voice-to-text), patient alerts and suggestions, and a full mobile and web experience. The platform launched in the US and now serves 1,000+ patients with 4,000+ registrations. That's what I build — not prototypes, not MVPs held together with shortcuts. Production systems that handle real patients, real clinical data, and real compliance requirements. WHAT I BUILD FOR HEALTHCARE → Mental Health Platforms — End-to-end therapy apps: patient intake, mood tracking, session management, crisis alerts, and AI-driven suggestions → Voice AI & IVR — Twilio IVR, voice-to-text, AI voice intake, and real-time transcription using Azure Speech and medical scribe workflows → AI Chatbots for Clinical Use — HIPAA-compliant chatbots for patient intake, symptom collection, mood check-ins, and escalation triggers → Telehealth & Therapy Session Tools — Video consultations, session notes, treatment tracking, and provider dashboards → HIPAA-Compliant Azure SaaS — Secure, auditable healthcare platforms built on Azure App Services, SQL Database, Key Vault, and RBAC → EHR Integration — Epic and AthenaHealth API integrations, HL7/FHIR data pipelines, and patient record sync → Mobile & Web — React Native mobile apps + Next.js/Blazor web platforms — one codebase, consistent clinical experience PROVEN RESULTS (not estimates) → Mental Health Platform (US) — 1,000+ patients served, 4,000+ registrations, voice AI intake, mood tracking, AI chatbot, crisis alerts, medical scribe — live in production → Clinic Management SaaS — 3,000+ patient check-ins/month across 20+ clinic locations — real-time digital queue, SMS alerts, multi-site ops — 3-year engagement, $76K+ → Medical Document Intelligence — Azure AI Search for clinical records; document retrieval from hours to minutes → RAG Clinical Data Pipeline — Automated clinical reporting replacing multi-day manual process with same-day automated output WHY HEALTHCARE CLIENTS STAY FOR YEARS Healthcare apps fail for one of three reasons: they're not built to clinical workflow, they're not HIPAA-compliant by architecture, or they fall apart when the patient volume scales. I design for all three from day one. HIPAA compliance isn't a checkbox I add at the end — it's in the data model, the API design, the Azure configuration, and the audit logging. Clinical workflows aren't assumed — they're mapped before a single line of code is written. I hold 5 Microsoft Azure certifications (AZ-900, AZ-204, AZ-104, AZ-305, AZ-400). I've integrated with Epic and AthenaHealth. I've built voice AI intake with Twilio. I've shipped mobile and web healthcare apps to real US patients. My clients don't come back because I'm available. They come back because what I built is still running — and still growing. MY HEALTHCARE STACK Backend: ASP.NET Core · C# · .NET 8 · Blazor · Node.js · Entity Framework Azure: App Services · Functions · Azure Speech · Cognitive Services · OpenAI · AI Search · Blob · Key Vault · SQL Database Voice AI: Twilio IVR · Voice-to-Text · Medical Scribe · Real-Time Transcription AI / Chatbot: Azure OpenAI · LangChain · RAG Pipelines · NLP · Mood Analytics Mobile & Web: React Native · Next.js · Blazor · Tailwind CSS EHR / Interoperability: Epic API · AthenaHealth API · HL7 · FHIR Compliance: HIPAA-compliant architecture · Azure RBAC · Audit logging · Secure API gateways DevOps: Azure DevOps · GitHub Actions · Docker · CI/CD Pipelines MY PROCESS → Step 1: Map the clinical workflow — patient journey, provider needs, compliance requirements → Step 2: Design HIPAA-compliant Azure architecture — security, audit, and data model built in from the start → Step 3: Build mobile and web in parallel — consistent experience across all patient touchpoints → Step 4: Integrate AI features — voice intake, chatbot, mood tracking, medical scribe — incrementally and testably → Step 5: Connect to EHR systems (Epic, AthenaHealth, FHIR) where required → Step 6: Deploy with CI/CD, monitoring, and full documentation — then stay available as the platform grows IS THIS YOU? → You're building a mental health or clinical app and need a developer who understands both the tech and the clinical workflow → You need HIPAA compliance built in — not bolted on after launch → You want voice AI, chatbot, or medical scribe features and need someone who has shipped these in production → You need mobile and web — and don't want two separate teams