Your data is in five places. Your reports are in Excel. Your leadership is making decisions on last month's numbers. I fix that, and I build it right the first time. I handle the full project: requirements gathering, data modelling, ETL pipeline design, report development, stakeholder customisation, and handover documentation. Clients come to me to fix existing dashboards and data models, or to consolidate data spread across multiple systems into a single source of truth everyone can trust. My background covers 5+ years of analytics and data engineering work across multiple industries. I hold a B.S. in Information Science from CU Boulder, am finishing an M.S. in Data Science at CSU Global, and hold the DP-600 Microsoft Fabric Analytics Engineer certification. I am also a licensed commercial pilot, so you can expect proactive communication and sound judgment under pressure. All of my previous clients and employers will provide positive references upon request. Here is a sample of recent work: For a multi-property hotel group in Canada, I built a 6-page Power BI dashboard from scratch. The data model used a star schema with six fact tables connecting financial, maintenance, revenue, people, reputation, and operational data. Each page was designed around the specific needs of the department using it, based on direct input from those stakeholders. I owned the project end-to-end from the first requirements conversation through deployment and documentation. For an e-commerce company, I built a 7-page sales dashboard with the specific KPIs a major retail partner required before signing a vendor agreement. The same underlying data model was adapted for internal reporting at every level of the business, with access scoped by role. I maintained that dashboard and the ETL pipeline behind it for two years as requirements evolved. I also built a predictive model to replace a departing purchasing manager, handled a full CRM migration to HubSpot that a vendor had quoted at $5,000, but I completed it for $500, and conducted market basket analysis that informed website development. At a nonprofit, I built Power BI and Tableau dashboards to track program KPIs, grant performance, and conversion-funnel analytics. I led the ETL work for an applicant-tracking system and produced financial dashboards supporting grant proposals totalling over $1M. I integrated GA4 with Google Ads, built a funnel report that identified where users were abandoning the page before donating or reaching out, and collected and analysed data for a study on paralysed veterans presented to CU Anschutz. At a legal office, I deployed a Power Apps application to automate reception duties and build a structured data record that fed into a Power BI dashboard that stakeholders monitored on an ongoing basis. Earlier in my career, I worked at the National Centre for Atmospheric Research, where I aggregated and processed large-scale time-series climate datasets on the Cheyenne high-performance computing system. That work built the data engineering foundation I bring to every project now. Power BI is my primary platform and where I do my deepest work. It connects to virtually any data source, and I work with all of them: SQL databases, Snowflake, AWS (S3 and Athena), flat files, APIs, Excel, SharePoint, and GA4. If it has data, I can connect it, model it, and build reliable reporting on top of it. For Snowflake and Microsoft Fabric specifically, I work the full stack: star schema design, ETL loading via Apache Airflow and dbt, performance tuning, and resolving access and permission issues between systems. I work with a fixed scope and clear deliverables at each stage, so there are no surprises mid-project. Before I write a single measure or build a single visual, I make sure I understand what decisions the output needs to support. Stack: Power BI, DAX, M, Power Query, SQL, Python, R, Snowflake, Microsoft Fabric, AWS (S3, Athena), Apache Airflow, dbt, Power Apps, Looker Studio, GA4

I am a Senior Data Engineer with 10+ years of experience designing and implementing large-scale data pipelines, cloud data warehouses, and streaming solutions. I hold a proven track record of delivering high-performance, reliable, and cost-optimized data platforms for enterprises across multiple industries. 💡 Core Expertise: - Data Warehousing: Snowflake, Amazon Redshift - ETL / ELT: dbt, Python, Airflow, AWS Glue, Fivetran - Streaming & Messaging: Apache Kafka, Kinesis - Cloud Platforms: AWS (S3, Lambda, EC2, EMR, RDS, IAM, CloudFormation) - Databases & Querying: SQL, PostgreSQL, DynamoDB, MySQL - Big Data Tools: Spark, Hadoop (where relevant) ✅ What I Deliver: - Scalable data pipelines (batch & real-time) - Optimized Snowflake & Redshift architectures - ETL/ELT frameworks ensuring data quality & governance - AWS-based cloud data solutions with cost optimization - Kafka streaming integrations for real-time analytics ⭐ Why Hire Me? - 10+ years in data engineering & cloud ecosystems - 5-star reviews from clients on Upwork & Fiverr - Strong problem-solving, communication, and end-to-end delivery focus - Experience working with startups to Fortune 500 enterprises

With an MS in Data Science and 7+ years of industry experience, I architect end-to-end data solutions that bridge the gap between theoretical innovation and practical business impact. Core Competencies 🎯 Data Science Leadership 📊 Orchestrate sophisticated data pipelines from extraction to deployment, leveraging advanced statistical methods and machine learning algorithms Drive business decisions through predictive modeling and deep learning implementations using PyTorch, TensorFlow, and scikit-learn Pioneer automated A/B testing frameworks that accelerate decision-making cycles Analytics & Visualization 📈 Craft compelling data narratives through interactive dashboards using Power BI, Tableau, and custom Python visualizations Design real-time monitoring solutions with Google Data Studio and Klipfolio Implement advanced time series forecasting using LSTM, GRU, and ARIMA models Technical Arsenal ⚡ Cloud & Infrastructure ☁️ Azure: Azure Databricks, Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, Azure Stream Analytics, Azure Functions, Azure DevOps, Azure Cosmos DB AWS: Amazon EMR, AWS Glue, Amazon Redshift, Amazon S3, Amazon Athena, AWS Lambda, Amazon QuickSight, Amazon SageMaker, DynamoDB GCP: BigQuery, Dataflow, Cloud Storage, Pub/Sub, Dataproc, Cloud Functions Data Processing & Storage 🔄 Big Data: Apache Spark, Hadoop, Hive, Delta Lake, Databricks Databases: PostgreSQL, MongoDB, Cassandra, Redis, Snowflake Stream Processing: Apache Kafka, Apache Flink, Apache NiFi Data Quality: Great Expectations, dbt, Apache Griffin Machine Learning & AI 🤖 Frameworks: PyTorch, TensorFlow, scikit-learn, Keras, XGBoost, LightGBM MLOps: MLflow, Kubeflow, DVC, Weights & Biases Experimentation: A/B Testing, Multi-armed Bandits AutoML: H2O.ai, AutoKeras, TPOT Development & DevOps 🛠️ Containerization: Docker, Kubernetes CI/CD: Jenkins, GitHub Actions, GitLab CI IaC: Terraform, Ansible, CloudFormation Monitoring: Prometheus, Grafana, ELK Stack Analytics & BI 📊 Visualization: Power BI, Tableau, Looker, QuickSight, Qlik Sense Python: Pandas, NumPy, Matplotlib, Seaborn, Plotly SQL: Advanced SQL, Window Functions, Performance Tuning Notebooks: Jupyter, Databricks Notebooks, Google Colab Signature Achievements 🏆 Reduced data processing time by 60% through optimized ETL pipelines and distributed computing Implemented ML models achieving 85%+ accuracy in predictive maintenance Architected scalable data warehouses handling 10TB+ of structured and unstructured data 📫 Open to collaborations and innovative data projects 🌟 Azure Certified: DP-203 Data Engineer Associate 🏅 Azure Certified: DP-900 Azure Data Fundamentals #DataScience #MachineLearning #Azure #AWS #Python #DeepLearning #ETL #DataEngineering #BigData #AI #DataVisualization #NLP #TimeSeries #CloudComputing #DataArchitecture #DataAnalytics #BusinessIntelligence #PredictiveAnalytics #DataPipelines #DataModeling #Databricks #Snowflake #MLOps #DevOps #Kubernetes #Terraform #ApacheSpark #DataLake #DataWarehouse #StreamProcessing

Data & Analytics Engineer specializing in workflow automation, data pipelines, and business intelligence solutions. From Google Sheets automation to production-grade data stacks — I build systems that save time, reduce errors, and turn raw data into decisions. What I can help you with: 🔹 Spreadsheet & No-Code Automation — Google Sheets, Google Apps Script, Zapier, Excel. Custom dashboards, automated reports, data entry systems, and API integrations. If you're doing something manually that could run on its own, I'll build it. 🔹 Data Engineering & Pipelines — Python, SQL (MySQL, PostgreSQL), Apache Airflow, Apache Spark, dbt, Snowflake. End-to-end pipelines from raw ingestion to clean, reliable data your team can trust. 🔹 Analytics Engineering & BI — Looker (certified), Power BI, data modeling, and reporting layers built for non-technical stakeholders. I translate business questions into metrics that actually get used. 🔹 AI & Modern Data Stack — Experience with LLM integration, AI-assisted workflows, and modern tooling including dbt, Snowflake, and cloud-based orchestration. Why clients keep coming back: ✔ Fast response time — typically under 2 hours ✔ I understand your problem, not just your task — I'll push back if there's a better solution ✔ 100% Job Success Score across 36+ completed contracts ✔ Bilingual — English & Spanish Whether you need a quick automation fix or a full data infrastructure built from scratch, let's talk.

I am a Data Engineer with 6+ years of experience specializing in SQL, Snowflake, Azure, Python, and ETL processes. I help businesses design, optimize, and automate data pipelines for efficient data management and analytics. 🔹 Expert in SQL & Data Warehousing – Writing optimized queries, performance tuning, and database design. 🔹 Cloud & Big Data Solutions – Hands-on experience with Azure Data Factory, Microsoft Fabric, and Snowflake for scalable data processing. 🔹 ETL & Data Migration – Building robust data pipelines using Python, PySpark, Apache NiFi, and IBM DataStage. 🔹 Problem-Solving & Optimization – Identifying and resolving performance bottlenecks to ensure seamless data workflows. I have worked on client-facing projects, collaborating with cross-functional teams in agile environments. Whether it’s SQL query optimization, data integration, or cloud-based solutions, I deliver efficient and cost-effective results. Let’s connect and discuss how I can help with your data challenges!

𝐃𝐞𝐯𝐎𝐩𝐬 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 + 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 = 𝐅𝐚𝐬𝐭𝐞𝐫 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬, 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐜𝐥𝐨𝐮𝐝 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 I’m a 𝐃𝐞𝐯𝐎𝐩𝐬 & 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 with strong expertise in multi-cloud platforms (𝐀𝐖𝐒, 𝐀𝐳𝐮𝐫𝐞, 𝐆𝐂𝐏) and a solid background in Python backend development, ETL/ELT pipelines, and data workflows. I specialize in designing cloud-native systems that are secure, automated, and optimized for performance and cost. 𝐇𝐨𝐰 𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮: - Automate CI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps) - Deploy & manage infrastructure with Terraform, Ansible, Docker, Kubernetes - Build reliable ETL/ELT pipelines for data ingestion, transformation, and analytics - Develop Python backend services for APIs, microservices, and automation - Manage cloud databases (SQL & NoSQL) and data warehouses (BigQuery, Redshift, Snowflake, Azure Synapse) - Integrate workflows with dbt, Airflow, and cloud-native data services - Cloud monitoring, logging, and cost optimization (CloudWatch, Azure Monitor, GCP Logging, Grafana) 𝐒𝐤𝐢𝐥𝐥𝐬 & 𝐓𝐨𝐨𝐥𝐬: - Cloud Platforms: AWS, Azure, GCP - DevOps: CI/CD, Terraform, Ansible, Jenkins, GitHub Actions, Docker, Kubernetes - Data Engineering: ETL/ELT, dbt, Apache Airflow, Data Warehousing - Programming: Python, Bash, SQL - Databases: MySQL, PostgreSQL, DynamoDB, MongoDB, BigQuery, Redshift - Monitoring & Logging: Prometheus, Grafana, ELK, CloudWatch, Azure Monitor 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: - Automated multi-cloud CI/CD pipelines that reduced release cycles by 60% - Built scalable ETL workflows moving terabytes of data daily across AWS & GCP - Migrated on-premise systems to Azure and AWS with zero downtime - Designed Kubernetes clusters to run both data pipelines and microservices efficiently I focus on clear communication, fast delivery, and building systems that scale with your business needs. 𝐋𝐞𝐭’𝐬 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 𝐚𝐧𝐝 𝐝𝐢𝐬𝐜𝐮𝐬𝐬 𝐡𝐨𝐰 𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐰𝐢𝐭𝐡 𝐃𝐞𝐯𝐎𝐩𝐬 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧, 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐜𝐥𝐨𝐮𝐝-𝐧𝐚𝐭𝐢𝐯𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐝𝐫𝐢𝐯𝐞 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐟𝐨𝐫𝐰𝐚𝐫𝐝!

With over 18 years of experience in Full-Stack Development (MERN, Python), AI, and Data Engineering, I have a proven track record of building and scaling web applications, data pipelines, and real-time systems for enterprise-level customers. As a Top-Rated Plus freelancer with a 100% Job Success Rate and over $50K earned on Upwork, I specialize in MERN stack development, API integration, and cloud infrastructure to deliver scalable, high-performance ERP applications and complex systems. Key Expertise: As a MERN Stack Developer, I specialize in React.js and Node.js with expertise in designing scalable service APIs (REST, SOAP, GraphQL, Microservices) hosted on cloud environments like AWS. I excel in performing code reviews, ensuring optimal performance, and writing reusable code that enhances scalability and security. Full-Stack Development Expertise: Expertise in React.js (Hooks), Node.js, MongoDB, Express.js, and Apollo for front-end and back-end development. Experience building and optimizing secured backend APIs consumed by multiple applications (web and mobile). Developing highly scalable APIs using GraphQL, RESTful services, and Microservices architecture. Building complex ERP systems with enterprise-level clients, ensuring performance, scalability, and security. Experience with Socket.IO for real-time communication. Cloud Infrastructure: I bring hands-on experience with AWS services like EC2, S3, RDS, Lambda, ElasticSearch, API Gateway, and more. My proficiency in cloud infrastructure ensures that applications are designed, deployed, and managed in a highly scalable and secure manner. Cloud and DevOps Expertise: AWS EC2, S3, Lambda, ElasticSearch, Elastic Beanstalk, API Gateway, RDS, SQS, EC2 Container Service for cloud environments. CI/CD pipelines with GitLab, Jenkins, and automated testing to deliver reliable and efficient software solutions. Security implementation using OAuth 2.0, JWT, and SSL/TLS. Responsibilities & Key Contributions: As part of large-scale projects, I provide code review services to ensure that the code meets industry standards and best practices. I also contribute by fixing bugs and implementing new modules as needed to ensure smooth delivery and continuous improvement of the application. Advanced Skills: Writing reusable code and optimizing applications for scalability and performance. Experience with testing frameworks and implementing automated testing platforms like NUnit, JUnit, and others. Deep understanding of back-end systems using Koa, Oracle, Redis, and MongoDB. Ensuring best practices in UI/UX design and responsive design using React.js with Tailwind CSS and Material-UI. Experience & Education: Bachelor’s in Computer Science with a deep technical background in full-stack development. Over 18 years of experience in building, scaling, and optimizing full-stack web applications for enterprise-level customers. Recent Success: I’ve successfully completed 28+ projects on Upwork, earning over $50K in revenue, with a reputation for delivering scalable, high-performance ERP systems and enterprise-grade solutions on time. Skills & Expertise: Front-End Development: React.js, Redux, HTML5, CSS3, Tailwind CSS, Material-UI Back-End Development: Node.js, Express.js, Apollo, Koa, MongoDB, Redis, Oracle API Development: REST, GraphQL, Microservices Cloud: AWS (EC2, S3, Lambda, ElasticSearch, RDS, API Gateway, Elastic Beanstalk) DevOps: CI/CD pipelines (GitLab, Jenkins), Docker, Kubernetes Testing & Security: Automated testing platforms, OAuth 2.0, JWT, SSL/TLS I am seeking a long-term, full-time contract opportunity where I can bring my expertise in the MERN stack to develop complex applications, perform code reviews, and contribute to the success of your enterprise-grade projects. Let’s work together to build robust, scalable, and efficient solutions that drive business success!

Data engineer building scalable pipelines, cloud platforms, and analytics solutions that transform messy data into reliable business decisions.

I build production-grade ML systems and agentic automation infrastructure that transform raw data into real-time decisions, workflows, and execution systems. My work sits at the intersection of: * Machine learning systems (forecasting, prediction, optimization) * Agentic workflows (multi-step automation, decision agents) * Real-time data infrastructure (APIs, pipelines, monitoring systems) The outcome is simple: systems that don’t just analyze data — they act on it. What I Build 1. ML Forecasting & Predictive Systems Production-ready time-series and predictive pipelines: * Demand, revenue, and behavioral forecasting systems * Prophet and deep learning-based models * Automated retraining and versioned inference APIs * Real-time scoring for live production environments 2. Optimization & Decision Engines Turning constraints into executable systems: * MILP-based optimization (routing, allocation, scheduling) * Policy simulation and scenario modeling * Resource allocation and tiering engines * Decision automation tied to live data streams 3. Agentic Automation Systems (LLM + Workflow Agents) Autonomous multi-step systems that execute workflows: * Multi-agent orchestration pipelines * Workflow automation and decision simulation systems * Signal extraction → reasoning → action pipelines * Event-driven agents for monitoring and execution 4. Real-Time Data Infrastructure Production-grade data systems for high velocity environments: * FastAPI-based inference and orchestration layers * Event-driven pipelines (APIs, SFTP, streaming sources) * Time-series databases (TimescaleDB, etc.) * Monitoring, logging, and failure recovery systems 5. Financial & Risk Systems Applied ML for high-stakes decision environments: * Credit risk scoring and financial analytics pipelines * Trading signal generation and backtesting systems * P&L simulation and scenario modeling * Structured decision support systems Selected Production Work Real-Time Telecom Forecasting System Built a forecasting and matching engine processing 30,000+ users with 15-minute refresh cycles on Azure, replacing manual allocation logic with a production ML + API system. Multi-Agent Intelligence System (PulseAgent) Designed a multi-agent workflow system that orchestrates reasoning and execution across data sources for automated decision-making. Decision Intelligence Engine (Deal Intelligence Agent) Built a signal extraction and ranking system that converts raw data into actionable business intelligence using ML + agent workflows. Automated QA & Monitoring System Developed a production monitoring system for validating pipelines, detecting failures, and ensuring system reliability in real time. Credit & Financial Risk Automation System Engineered a financial analytics engine for credit evaluation, ratio modeling, and automated risk scoring How I Work I design systems with production reality in mind: * Every model is deployed behind an API or workflow engine * Every system includes observability and failure handling * Every output is traceable, testable, and reproducible * Every pipeline is built for real operational use, not demos If it doesn’t run reliably in production, it doesn’t ship. Who I Work With * Startups building AI-driven products * Fintech and analytics teams * Companies automating decision workflows * Teams moving from manual analysis → autonomous systems Let’s Work Together If you need systems for: * Forecasting and prediction at scale * Optimization and decision automation * Agentic workflows or AI-driven systems * Real-time ML infrastructure in production I can design and deploy the full system end-to-end.

✅ 100% Job Success - 5 Star Review - Satisfied clients I architect cloud platforms, automate reporting, and fix data workflows. Recent results: - Migrated a fintech workflow from fragile private-server scripts to an automated Google Cloud platform - Reduced a global retailer's GCP costs by 30% - Built reliable data systems supporting analytics and machine learning at scale How I help: - Architect scalable cloud platforms and landing zones - Automate reporting and manual workflows - Build dashboards on top of reliable data foundations - Fix unreliable data pipelines and reporting systems - Reduce cloud costs and reporting delays If you need better reporting, more reliable workflows, or a cloud platform that can scale, let's talk.