⌚ Available from 6am - 11 pm Pacific time 🚀 Skyrocket Your Business with Cutting-Edge AI Solutions! 🚀 🇺🇸🇪🇸 Bilingual I help businesses transform AI from an experiment into a measurable business advantage. Whether you need an AI-powered chatbot, a production-grade RAG system, intelligent automation, predictive analytics, or a scalable machine learning platform. I’ve worked across Generative AI, Data Science, Machine Learning, Analytics, and Data Engineering to help organizations automate workflows, improve decision-making, reduce operational costs, and create new revenue opportunities. What I Can Help You Build 🤖 Generative AI & LLM Solutions * RAG (Retrieval-Augmented Generation) systems * AI Agents & multi-agent workflows * GPT-4, Gemini, Claude, Llama, and Mistral applications * Custom copilots and knowledge assistants * Prompt engineering and model optimization * LLM evaluation, monitoring, and deployment 💬 AI Chatbots & Virtual Assistants * Customer support automation * Internal knowledge assistants * Conversational AI platforms * Dialogflow, Vertex AI, Botpress, and RASA solutions * CRM, API, and database integrations 📊 Data Science & Advanced Analytics * Predictive modeling and forecasting * Customer and business intelligence analytics * Dashboard development (Power BI, Tableau, Looker Studio) * Statistical analysis and experimentation * End-to-end machine learning pipelines 🔁 Data Engineering & Cloud Architecture * ETL/ELT pipeline development * Data warehouses and lakehouses * Snowflake, BigQuery, Redshift, and Databricks * Data quality, governance, and orchestration * Scalable cloud-native architectures 👁️ Computer Vision & NLP * OCR and document intelligence * Image classification and object detection * Sentiment analysis and text mining * Custom NLP workflows and automation Core Technologies AI & ML: OpenAI, GPT-4, Gemini, Claude, Llama, Mistral, LangChain, Hugging Face, TensorFlow, PyTorch Data & Analytics: Python, SQL, Spark, Tableau, Power BI, Looker Studio, dbt Cloud & Data Platforms: AWS, Google Cloud, Azure, Snowflake, BigQuery, Redshift, Databricks If you’re looking for a partner who can bridge business goals with technical execution and deliver production-ready AI solutions, let’s discuss your project.

📊📈 Unlock the full potential of your data with clean pipelines, smart automation, and dynamic reporting systems. I build end-to-end analytics solutions - from setting up databases and API integrations to transforming, analyzing, and visualizing your data using the most effective tools for the job. Whether it’s organizing scattered sources or automating complex reports, I make sure your data is accessible, reliable, and ready to drive confident decisions. 🔧 Tools & Technologies 🔴 Data Visualization & Analytics: Power BI, Tableau, Looker & LookML, Looker Studio 🔴 ETL & Automation: dbt, Power Query, Supermetrics, Airbyte, AWS Lambda, AI-assisted workflows 🔴 Cloud, Databases & Warehousing: PostgreSQL, Snowflake, AWS RDS, AWS Redshift, Supabase 🔴 Platforms: Shopify (Advanced Reporting), Google Analytics 🧠 What I Deliver 🟠 End-to-End Reporting Systems 🟠 Interactive Dashboards & KPI Reporting 🟠 E-Commerce & Marketing Analytics 🟠 Automated Data Pipelines 🟠 Custom Metrics & Performance Tracking 🟠 SQL Modeling & API Integrations 🟠 Clean, Scalable, Structured, and Reliable Data Infrastructure I can turn your data into a system that works - clean, scalable, and built for insights. Have something in mind? Reach out and let’s talk about how we can move it forward.

Top Rated Plus | 100% Job Success | Founder, DataNicely I help growing organizations fix reporting that can't keep pace with the business — when metrics don't align, dashboards aren't trusted, and leadership needs clearer, more reliable answers. I design analytics and reporting systems executives can depend on and teams actually use, enabling decisions to be driven by consistent, trusted data instead of spreadsheets and guesswork. As founder of DataNicely, I partner with leadership teams to bridge the gap between analytics strategy and execution — from defining KPIs and reporting roadmaps to building automated dashboards and data pipelines that eliminate manual effort. I also build AI agents on top of that clean data layer, so teams can ask questions of their data in plain language, specific to their role, without needing a data analyst in the room. With over 13 years of experience in analytics and data engineering, I've led initiatives across financial services, restaurant groups, nonprofits, and e-commerce — helping organizations centralize data, align metrics, and make confident, data-driven decisions. ⸻ Core Services • Analytics infrastructure design and data strategy consulting • Tracking cleanup and implementation (GA4, GTM, Meta Pixel, Conversions API) • AI-powered analytics agents (built on leading LLMs) • KPI definition, metric alignment, and reporting frameworks • Dashboard development (Power BI, Tableau, Sigma Compute, Looker Studio) • ETL/ELT pipelines, data integration, and workflow automation (BigQuery, dbt, Make, Python) • Automated reporting, monitoring, and real-time alerting ⸻ Tech Stack BigQuery | dbt | GA4 | GTM | Meta Pixel | Make | Power BI | Tableau | Snowflake | Looker Studio | Python ⸻ If your data infrastructure isn't keeping up with your business, that's the problem I solve — from the ground up.

Building data systems that don't just work—they perform at scale. With over 15 years of experience, I bridge the gap between complex data engineering and business-critical insights. Whether you are dealing with 200k+ RPS high-load environments or building a Modern Data Lakehouse from scratch, I architect solutions that are cost-effective and future-proof. What I bring to your project: - High-Scale/High-Load Expertise: Proven track record in building real-time pipelines (Flink, Spark, Kafka) that handle massive throughput with sub-second latency. - Modern Lakehouse Architecture: Specialist in implementing Delta, Hudi, and Iceberg to provide ACID transactions on top of scalable cloud storage. - Performance OLAP: Deep expertise in ultra-fast query layers using StarRocks, ClickHouse, and SingleStore to slash BI dashboard wait times. - Enterprise Governance: Deploying OpenMetadata and dbt to ensure your data is clean, documented, and ready for LLM/AI applications. 🛠️ Tech Stack & Ecosystems - Processing & Streaming: Apache Flink, Spark, Kafka (Streams/Connect), NiFi, Polars. - Lakehouse & Storage: Hudi, Iceberg, Delta Lake, Databricks, Apache Hadoop. - High-Performance DBs: StarRocks, ClickHouse, Vertica, SingleStore, Oracle. - Cloud & Orchestration: AWS, GCP, Azure, Kubernetes (K8s), Airflow, Prefect. - Analytics Engineering: dbt, BigQuery, CRM/Ads Integration, Medallion Architecture. Why work with me? I don't just "install" tools. I design systems that optimize for Compute Cost (FinOps) and Data Reliability. If your current infrastructure is lagging or your cloud bills are spiraling, I can audit your stack and implement a high-performance alternative. Ready to scale? Let’s talk about your data architecture.

I am a Lead Data Scientist and Data Engineer with over 10 years of experience turning messy, complex datasets into scalable, high-accuracy predictive systems and automated data pipelines. I specialize in the Retail/FMCG and Healthcare/Pharmaceutical sectors, bridging the gap between advanced technical architecture and actionable business strategy. Backed by an Executive PG in Data Science & AI from IIT Roorkee and holding Microsoft Certifications in Python and Azure (AZ-900 & DP-100), I build the "intelligence layers" that drive enterprise decision-making. My Core Technical Expertise: Data Engineering & ETL: GCP (BigQuery), SQL, dbt, Parquet, Batch Processing, Multiprocessing Programming & AI/ML: Python (Pandas, Scikit-learn, Regex, Flask, Streamlit), R, SAS, Predictive Modeling, GenAI Business Intelligence & Viz: Power BI, Looker, Clinical Dashboards Proven Domain Impact: Retail & FMCG: Engineered automated POS data pipelines (36-month initiative), improving loading efficiency by 30%. Led a 24-month product harmonization project using predictive modeling to resolve discrepancies across millions of records. Designed GCP/BigQuery demand forecasting pipelines to optimize supply chain inventory. Healthcare & Clinical Analytics: Deep experience integrating open-source tools (Python, R, SAS) for clinical trial data visualization. Proficient in building interactive clinical dashboards utilizing Pharmaverse, SDTM, and ADaM datasets. Cloud Architecture & Metrics: Migrated massive datasets to modern cloud environments and developed robust DAU/MAU tracking structures inside BigQuery and Looker. Why Work With Me? I do not just execute code; I solve business problems. Alongside my technical career, I manage my own coworking space business, meaning I inherently understand the commercial urgency, stakeholder management, and extreme ownership required to deliver ROI. Whether you need to deploy an AI model, untangle enterprise ERP data, or build an interactive dashboard from scratch, I write clean, maintainable, and production-ready code. Let’s connect to discuss how we can scale your data infrastructure and build models that actually impact your bottom line.

🏆 Upwork Top Rated Talent 2025 | ⭐️ 100% Successfully completed jobs Hello 👋, I am a full-stack data scientist and data engineer with over 5 years of professional experience. I am familiar with almost all major tech stacks on data science/engineering. My main areas of expertise and tech stack: Data Science: Python, R, C++, NLP, HuggingFace, ML, llama 3.1, Recommendation systems, Deep Learning, Computer Vision, Anomaly Detection, Chatbot, Price Optimization and Predictions, Time Series Analysis and Stock Predictions, etc. AWS: IAM, S3, EC2, Glue, Redshift, Athena, Lambda, Step function, Kenisis, EMR, Cloud formation, Lake formation Data Warehouse: BigQuery, Redshift, MS SQL, MySQL, PostgreSQL, Snowflake, and Databricks. ETL: aws glue, pySpark, mage, airflow, airbyte, dbt, scriptella, informatica, nifi, stitch, skyvia Business Intelligence: Tableau, Power BI, Google Looker Studio, Seaborn, ggplot2, Apache Superset, Metabase, Plotly, folium Web scraping: Scrapy, urllib3, Selenium, Beautifulsoup Web Analytics: Google Tag Manager, Google Analytics 4, Universal Analytics to GA4 migration. Data Sources: Shopify, Meta Ads, Google Ads, TikTok Ads, Pinterest Ads, LinkedIn Ads, Google Analytics, Google Search Console, GoParrot, SQUARE API, Quickbooks, SEMRush, etc. Software Design Documentation: graphviz, mermaid Project Management and Documentation: Confluence, Jira, Notion, ClickUp, Trello I’ve worked with multiple e-commerce companies, helping them achieve over $10 million in sales with just $2 million in spend by leveraging data-driven strategies across marketing, product, and operations. In addition, I’ve collaborated with SaaS companies across various departments to optimize performance and accelerate growth using data insights. My background also includes roles as a FinCrime Analyst at Revolut, where I specialized in analyzing complex financial data to detect fraud and financial crime, and as a Chatbot Analyst at Afiniti, where I worked on natural language processing (NLP) and conversational AI systems to enhance user experience. Earlier in my career, I held Data Analyst positions at Ibex and Landing, where I worked on a range of analytics projects, gaining hands-on experience with data pipelines, dashboards, and performance optimization. This diverse experience has shaped me into a well-rounded data scientist with a deep understanding of how to drive business impact through data—especially in e-commerce, SaaS, and financial services.

Struggling with unreliable data that slows decisions? With 9+ years as a Data Engineer & AI Developer, I reduced ETL processing time by 80% and scaled BI adoption from 20 to 80+ users for fintech, SaaS, and e-commerce teams. I build Snowflake, dbt, and Python pipelines that turn raw data into trusted analytics. 🚀 HOW I CAN HELP YOU SNOWFLAKE DATA WAREHOUSE DEVELOPMENT Turn scattered data into a single source of truth that improves reporting speed and gives your team trusted numbers to act on. I’ve built scalable warehouses and dimensional models that supported self-service analytics and helped grow BI adoption from 20 to 80+ users. DBT ANALYTICS ENGINEERING Increase metric consistency and reduce reporting errors with clean transformation layers your team can trust. I’ve used dbt to build KPI frameworks and semantic layers that improved decision-making across product, marketing, and executive teams. ETL/ELT PIPELINE OPTIMIZATION Cut processing time and keep data flowing even as volume grows. I reduced ETL runtime from 35 minutes to 7 minutes and have built pipelines handling millions of records daily with strong data quality controls. PRODUCT ANALYTICS DASHBOARDS Give your team clearer visibility into retention, conversion, and user behavior so you can make faster growth decisions. I’ve delivered dashboards and scoring systems that improved retention by 10%, trade success by 14%, and revenue by 12%. AWS DATA ENGINEERING AND AUTOMATION Automate your data workflows and reduce manual overhead across cloud systems. I’ve supported AWS migrations, built Airflow monitoring for delayed data, and designed near real-time systems with 99% data accuracy. 🏆 RESULTS "Originally we planned to do this project in Python. However, after further analysis, we decided to continue Node.Js module and migrate to Lambda. Ehtesham brought in Node.JS / Lambda expert to the project and helped to complete the project. Positive: Good Communication, Availability and Commitment to deliver." – Callkisan P., Client "He is responsive and takes responsibility well. He possesses good AWS, Python and SQL skills. We liked working with him." – Robb G., Client "Ehtesham was an excellent contractor. Knowledgeable, hard working, and responsible for the tasks required of him. Even when the project deadlines got pulled in, he worked hard to get us the results we needed to serve our client. Highly recommended." – Fred V., Client 🎖 Data Science Professional 🏅 Machine Learning Certification 🏅 Data Visualization and Communication with Tableau See full case studies in my portfolio below. 🤝 𝗚𝗘𝗧 𝗜𝗡 𝗧𝗢𝗨𝗖𝗛 Need a reliable data engineer or AI developer for Snowflake, dbt, Python, ETL, or analytics work? Message me on Upwork today for a free consultation and a clear plan to move your project forward. I typically respond within 4 hours and can help with urgent projects when needed. 𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸 I build reliable data systems in Snowflake and BigQuery, using dbt to transform models and Airflow to orchestrate pipelines. I write Python and SQL to automate ETL, validate data quality, and tune performance. I also use AWS to support scalable, production-ready analytics that stakeholders can trust. I build Snowflake data warehouses, automate ETL pipelines in dbt and Apache Airflow, and develop Python machine learning models for analytics.

I help companies modernize their data platforms using Microsoft Fabric, Azure, and Power BI so they can trust their reporting, eliminate manual processes, and make faster business decisions. I'm a Microsoft Fabric & Azure Data Architect with 10+ years of experience in Data Engineering and Business Intelligence. Previously, I served as Director of Data Analytics & Reporting for a US insurance company and have delivered enterprise data solutions across healthcare, insurance, retail, and SaaS industries. Why Clients Hire Me ✅ 10+ years of Data Engineering & BI experience ✅ Former Director of Data Analytics at a US Insurance Company ✅ Top Rated with 100% Job Success Score ✅ Delivered production ETL solutions for a 39-location healthcare network ✅ Built enterprise data warehouses and analytics platforms supporting thousands of business users ✅ 700+ Upwork hours with long-term client relationships My Approach 🔹Discovery & Requirements Gathering 🔹Architecture & Solution Design 🔹ETL & Data Pipeline Development 🔹Data Modeling & Governance 🔹Power BI Reporting & Visualization 🔹Testing, Documentation & Handover Core Services ✔ Microsoft Fabric Architecture ✔ Azure Data Engineering ✔ ETL Pipelines (ADF, Python, SQL) ✔ Data Warehouse Design ✔ Power BI Development ✔ Fabric Migration Projects ✔ Data Governance & Documentation ✔ Production Support Clients describe me as responsive, clear, and easy to work with — one wrote that I handled "a complex task involving cross-functional collaboration with multiple teams" and "adapted to changes" without dropping quality. Tell me about your current data platform and where your bottlenecks are. I'll provide a practical approach to modernizing your data architecture and analytics environment.

🏆 TOP RATED PLUS Data Expert | Top 3% on Upwork 💰 $600K+ earned | 16,000+ hours | 130+ clients served I am a Sr Data Engineer with expertise in developing robust AI Agents & Analytics layer. I bring over 8 years of hands-on experience with: - Building scalable ETL data pipelines that fetch raw data froms APIs and store it into data warehouses hosted over GCP/AWS (BigQuery | Snowflake | Redshift). - Developing Business Intelligence reports and dashboards using Data Studio (formerly Looker Studio), Metabase, Looker, Tableau etc. - Building powerful AI Agents using Gemini, Claude, OpenAI, Dialogflow. - Track user behaviour data using GA4 and Google Tag Manager. I’ve worked with 130+ clients across eCommerce (Shopify, WooCommerce), digital marketing & paid ads (Meta, Google Ads), mobile apps & gaming analytics, SaaS & web apps, and data-driven businesses in education, clean energy & media. 💡 What I do (End-to-End Ownership) 1. Data Engineering & Warehousing: - Build scalable, reliable data pipelines using BigQuery, Snowflake, Redshift, Python and APIs of data sources - Automated ETL (Airflow, APIs, Fivetran, custom Python scripts) - Single source of truth across marketing + product + revenue 2. Analytics & BI (Decision Systems, not just dashboards): - Executive dashboards (Looker, Data Studio (formerly Looker Studio), Metabase, Power BI, Tableau) - KPI frameworks aligned to revenue - Cohort, LTV, attribution & funnel analysis 3. Marketing & Web Tracking (Accuracy = $$$) - GA4, Google Tag Manager, Server-side tracking - Meta CAPI, Google Ads, TikTok tracking - Fix broken attribution & data loss 4. Generative AI & Automation - AI agents & workflows (OpenAI, Gemini, Claude) - Automate reporting, insights, and ops - Use AI where it actually improves ROI (not hype) 📈 Real Outcomes I’ve Delivered ✔ Built full marketing data warehouse → improved spend efficiency by 30%+ ✔ Fixed tracking & attribution → recovered lost revenue visibility ✔ Automated reporting → saved 20+ hrs/week for teams ✔ Delivered exec dashboards → faster, data-backed decisions 🧠 Why Clients Choose Me - I think like a business owner, not just an engineer - I focus on revenue impact, not vanity metrics - I handle end-to-end (tracking → pipelines → dashboards → insights) - Strong communication + fast execution (no hand-holding needed) 📈 My Tech Stack: - Business Intelligence & Data Visualisation: Google Data Studio (formerly Looker Studio) , Looker, Metabase, Power BI, Mode, Tableau, Databox, Zoho Analytics, DOMO, Google Sheets, etc. - AI: LLMs like OpenAI, ChatGPT, Gemini, Claude, DeepSeek, and GCP's services like Document AI, DialogFlow, CCAI, etc. - Engineering: SQL, Python, Airflow, APIs, Cloud Functions, Lambda Functions, Cloud Composer, Cloud Run - Data Warehouses: BigQuery, Redshift, MS SQL, MySQL, PostgreSQL, Snowflake, and Azure. - ETL & Webhook tools: n8n, Fivetran, Stitch, Windsor, Supermetrics, Power My Analytics, Saras Analytics, Zapier, Make, etc. - Tracking: Google Tag Manager, Google Analytics 4, Meta Ads Conversion API, Google Ads Conversion tracking, Stape, Server-side tracking. - Data Sources: Shopify, WooCommerce, BigCommerce, Meta Ads, Google Ads, TikTok Ads, Pinterest Ads, LinkedIn Ads, Apple Ads, Amazon Ads, Bing Ads, Google Analytics 4, Google Search Console, Google My Business, HubSpot, Active Campaign, PipeDrive, Facebook Page Insights, Instagram Insights, Stripe, SEMRush, MailChimp, Klaviyo, ClickUp, Ahref, etc. 👉 Let’s Work If you’re looking for someone who can own your entire data stack and turn it into a revenue engine, let’s talk. Click “Invite” and let’s discuss your use case 🚀 ----- 🔍 𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦 GA4, Google Analytics 4, Google Tag Manager (GTM), Server-side Tracking, Meta Conversion API (CAPI), Google Ads Conversion Tracking, Marketing Attribution, BigQuery, Snowflake, Redshift, Data Warehouse, Big Data, Data Engineering, ETL, ELT, Data Pipelines, Apache Airflow, Airflow DAGs, PySpark, Spark, Databricks, SQL, Python, Advanced SQL, Data Modeling, Data Transformation, Data Architecture, Data Lakes, Data Studio, Looker Studio, Power BI, Tableau, Data Visualization, Dashboard Development, Business Intelligence (BI), KPI Dashboard, Reporting Automation, Shopify Analytics, WooCommerce Analytics, Marketing Analytics, Product Analytics, Funnel Analysis, Cohort Analysis, LTV Analysis, Retention Analysis, Generative AI, OpenAI, ChatGPT, Gemini, Claude, AI Agents, AI Automation, AI Agents, LLM Applications, n8n, Workflow Automation, No-code Automation, Low-code Automation, Zapier, Make (Integromat), API Integrations, Webhooks, Stripe, HubSpot, Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads, Cloud Platforms (GCP, AWS), Cloud Functions, AWS Lambda, Data Orchestration

With 15+ years as a Data Engineer and M.S. in Math, my experience includes the development of ETL/Data Warehouse and Analytics dashboards for eCommerce, Marketing, and Banking. 𝙎𝙪𝙘𝙘𝙚𝙨𝙨 𝙎𝙩𝙤𝙧𝙞𝙚𝙨 1. Payments forecast with MAPE < 2%: Banks need to know their monthly cash inflow to plan new credits and attract investments. I have compiled historical payments from existing clients, organized by calendar month, loan date, and credit product. Used this info to iteratively forecast new payments from new credits. 2. Data Quality from 24% to 99%: fixed core table with account balances in a banking DWH. Analytics accounts in accounting equal the sum of the basic accounts. I made a list of clients with analytics and basic accounts that mismatch. Picked one basic account with the most zero balances across all clients. Statistically, it had the most chances of being wrong. Discussed it with the Admin of the source system, and after several find-and-fix iterations, he confessed there is a filter that does not export all the data into the DWH, enforced by security guys. 3. Credit scoring models development, reducing scoring costs by half: A Fintech company was looking for software to build an internal credit score. They were considering SAS, SPSS, and other software. I proposed to them to build a scoring model using the free R-project, and I was hired immediately. Have joined applications and credit histories data and built scoring models using logistic regression. After the AB test with underwriters, the model proved its efficiency and was deployed in production. We stopped paying for credit scores from 3d party provider reducing scoring costs by 2 times. 4. I was optimizing the legal collection process for a bank. The overall collection process starts with contacting the debtor and discussing the payment plan. If the debtor is unavailable or refuses to pay, the legal collection process starts. Lawyers fill in applications for the Magistrates’ court, paying some court fees. The court may decline the application if it reaches the court too late due to the statute of limitations or take the debtor’s side. Even a won case does not guarantee the debtor will pay. He might decide to file for bankruptcy. Given the high number of debts and fees involved in the court applications, legal collection is a costly process. Multiple outcomes make this process manually unpredictable. I was tasked with building a collection scoring model to filter those debts that are worth taking legal actions. I have pulled information on 100K debts that went through the legal collection process past 3 years. My target variable was the sum of payments after legal actions minus legal fees. I have tried many predictor variables, and the most informative were: • debt amount - the higher the debt, the higher the payment amount is expected • days past due - the higher the DPD, the higher the chances of hitting the statute of limitations • debotr’s region - courts' practices vary across the country, with some courts taking debtors’ side more often Once the model has been built, we applied it to the current debts in the legal process and found out that only 30% of debts are worth taking to court. The rest 70% could be sold to collection agencies. The legal collection scoring model helped to understand: • the factors that drive collection profits • automate manual decisions • increase collection profits • clear debts balances, selling them to professional collection agencies 𝙏𝙤𝙤𝙡𝙗𝙤𝙭 • ETL/Data Warehouse: SQL, dbt, Python, Redshift, BigQuery, SAS • Data sources: Shopify, GA4, Google Ads, Meta Ads • Analytics dashboards: Looker Studio, Looker, Power BI, Excel, Google Sheets • Finance math: ROAS, Gross margin, LTV, Cash Recovery • Data Science and ML: Credit Scoring and payments forecasting • Cloud tech: AWS, GCP, Docker • Technical writing: Business Proposal, Product Description, User's Guide, Installation Guide • Project management: Agile / Scrum Let's connect to discuss the details.