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Big Data Engineering Lead & Architect with strong experience in designing, building, and scaling enterprise-grade data platforms that enable data-driven decision-making. Specialized in Big Data ecosystems including Hadoop (HDFS, Spark, Hive, Impala, Sqoop, Kafka, Ranger and Apache NiFi), Cloudera, and Microsoft Azure technologies such as HDInsight, Azure Synapse, and Microsoft Fabric, with a proven ability to deliver secure, scalable, and high-performance data architectures. Experienced in leading engineering teams, architecting end-to-end data pipelines, optimizing performance, and enabling analytics and BI through Power BI and Tableau and SAP Business Objects alongside integration with SAP HANA and multiple data sources. Certified Cloudera Hadoop & Spark Developer and Tableau professional, with a strong focus on enterprise data strategy, cloud modernization, and delivering measurable business impact.

Need a backend, full-stack, or AI system that holds up in production, not just on a demo branch? That's what I do. I'm a senior software engineer with 6+ years building web apps, desktop products, data platforms, and enterprise integrations for clients in the US, Canada, and Europe. Clients usually come to me with one of these problems: - "Our API is slow or falls over under load." I profile it, fix the queries and caching, and add retries, idempotency, and monitoring so failures stop reaching your users. - "We need a real product, not a prototype." Secure auth, roles and permissions, billing, background jobs, licensing, automated tests, and CI/CD are part of the build, not a phase two. - "We want AI over our own documents but it keeps answering wrong." I build RAG pipelines with hybrid search, reranking, and measured retrieval quality so answers are traceable. - "Our legacy mainframe code needs to run somewhere modern." I deploy and repair COBOL/CICS/DB2/JCL systems on z/OS and IBM Cloud. WHAT I WORK WITH Backend: .NET Core, Python, FastAPI, Node.js, REST APIs, Clean Architecture, CQRS, Kafka, MassTransit, SignalR, Hangfire Frontend: Angular, React, Next.js, TypeScript, RxJS, NgRx, Tailwind, Material UI Data: PostgreSQL, SQL Server, MongoDB, Redis, Qdrant, pgvector AI: RAG, LangChain, OpenAI, Cohere, hybrid vector + BM25 search, reranking, evaluation Cloud and DevOps: AWS, Azure, IBM Cloud, Docker, GitHub Actions, Azure DevOps, xUnit, pytest Mainframe: z/OS, COBOL, CICS, DB2, VSAM, JCL WHAT I'VE DELIVERED - A multi-tenant vendor risk platform covering supplier onboarding through audits and remediation, with enterprise SSO, granular permissions, Stripe billing, and AWS deployment. - Three years on a freight-forwarding ERP: 1,000+ endpoints, 25+ carrier and accounting integrations, and the full rate-to-shipment lifecycle. - A licensed Windows and macOS desktop product with signed entitlements, offline grace periods, and crash-recovery testing. - A multilingual vehicle-manual chatbot with intent classification and an automated retrieval-evaluation harness. - Technical leadership of a 6-developer team across architecture, estimation, and releases. HOW I WORK I plan carefully before writing code, clarify anything that is unclear, and provide an honest estimate before development begins. I will also tell you early if an approach is risky or unlikely to work. You will receive working increments that you can review throughout the project - not a month of silence followed by unexpected results. I always take ownership of project and meet the deadline I commit to. I have maintained a 100% Job Success Score and delivered more than 2,400 hours of work on Upwork. Some of my project details are in my portfolio and work history below. Send me your requirements, and I will reply with a clear technical approach, the main trade-offs, and a realistic delivery timeline.

I help companies move, process, and use their data on AWS — from event-driven ETL pipelines to RAG-powered AI applications. For the past 4+ years I've built exactly that at the U.S. Securities and Exchange Commission, handling high-volume financial data. THREE THINGS I DO 1. DATA ENGINEERING — Design and build ETL/ELT pipelines with Apache Airflow, PySpark, AWS Glue, and Redshift/Iceberg. Batch or event-driven, greenfield builds or rescuing pipelines that keep breaking. 2. CLOUD ARCHITECTURE (AWS) — Architect serverless and containerized systems with Lambda, ECS, S3, and DataSync, all as infrastructure-as-code (AWS CDK, CloudFormation). Migrate legacy on-prem workloads to AWS with CI/CD from day one. 3. AI & LLM APPLICATIONS — Build RAG systems with vector search (Qdrant), LLM-powered document analysis, and AI features shipped inside real products (Python, Next.js, OpenAI API). RESULTS I'VE DELIVERED • Cut data availability from ~1 hour to minutes with event-driven AWS pipelines (SEC) • Reduced deployment cycles from 9 weeks to 4 by automating CI/CD for AWS Glue ETL jobs (SEC) • Built a private RAG document-Q&A system over internal docs — semantic search with no data leaving the org • Saved 75 analyst-hours per week by automating manual data workflows (Comcast) TECH STACK • Languages: Python, SQL, TypeScript/JavaScript, R, Golang • Data Engineering: Apache Airflow, PySpark, AWS Glue, Pandas, NumPy, Alteryx • AWS Cloud: S3, Lambda, ECS, EC2, Athena, Redshift, Iceberg, DataSync, RDS/Aurora, DynamoDB, SNS, CDK, CloudFormation, SAM, LocalStack, CloudWatch • AI / LLM: LLM API(Anthropic, Gemini, OpenAI), Vector DB, RAG pipelines, AI-assisted development with Claude Code & Cursor • Databases: PostgreSQL, MySQL, Redshift, MongoDB, Neo4j, DynamoDB, Supabase • Web & APIs: React, Next.js, Node.js, FastAPI, Flask, Prisma • DevOps: Docker, GitHub Actions, Bamboo, Splunk, Jupyter CERTIFICATIONS • AWS Certified Solutions Architect • AWS Certified Data Engineer HOW I WORK Clear communication, tested code, and documentation your team can actually maintain. Everything I build is infrastructure-as-code — reproducible in your environment, no black boxes, no key-person risk. Tell me about your data stack, your cloud setup, or the AI feature you want to ship — I'll reply with a concrete plan for how I'd build it.

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!

Most data pipelines don't fail loudly. They rot quietly. A job starts silently skipping rows. A dashboard still loads, but the numbers stopped matching reality weeks ago. The warehouse bill creeps up because nobody ever tuned the queries. Eventually someone exports to a spreadsheet "just to be sure" and that's the moment the system stopped being trusted. That's the work I do: build data pipelines and warehouses people actually rely on, and fix the ones that quietly stopped working. Over the last 3 years I've delivered 55+ data projects across finance, healthcare, energy, and e-commerce, from one-off ETL jobs to platforms processing billions of records a day. I work GCP-first (BigQuery, Airflow, dbt, Dataflow), and I'm comfortable across AWS, Postgres, and the messy real-world stack most teams actually have. What I build: - End-to-end ETL/ELT pipelines in Python, SQL, Airflow, and dbt - BigQuery / Snowflake / Redshift warehouses and data models that stay clean as they grow - Migrations off legacy jobs and on-prem databases — without losing data in the move - Metabase, Looker Studio, and Power BI dashboards your team will actually open - Query and cost optimization when your warehouse bill stops making sense - API integrations with proper logging, retries, and checkpoints, so failures are visible instead of silent You probably need me if: - Your pipelines break and you hear it from a stakeholder, not an alert - Reports run slow, cost too much, or quietly disagree with each other - A previous developer left and nobody fully understands the setup anymore - You're scaling fast and the current data stack is starting to crack A few real results: - Architected pipelines processing 5B+ records daily at 99% reliability - Cut a client's warehouse costs ~50% by migrating legacy jobs to BigQuery - 4× throughput and 70% faster ingestion on an API pipeline pulling 2K+ domains a day - 40% faster pipeline runs through Airflow optimization How I work: a clear yes/no on feasibility before you commit, regular updates, and no disappearing mid-project. Most clients come back — usually because fixing one thing surfaces the next. If that sounds like your situation, send a short note on what's breaking or what you're trying to build, and I'll tell you straight what it'll take.

I build AI systems that actually run in production — not demos, not prototypes, systems that real teams depend on every day. Over the past year I've designed and maintained end-to-end GenAI and automation pipelines for multiple clients: multi-agent voice assistants, HR screening tools, real-time database sync pipelines, and BI reporting suites. My work spans the full stack — from architecting the agent logic in LangChain and n8n, to orchestrating 37+ production Airflow DAGs syncing MSSQL, Oracle, and PostgreSQL, to building the frontend a client's HR team uses daily. I care most about reliability. I've patched broken third-party MCP servers, built guardrails against prompt injection and jailbreak attempts, and designed change-detection systems for databases with no reliable timestamps — the unglamorous work that keeps systems running when nobody's watching. If you need someone who can take an idea from "wouldn't it be nice if..." to a working, monitored, production system — let's talk.