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Indore, India
๐ TOP RATED PLUS Data Expert | Top 3% on Upwork ๐ฐ $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. Iโve 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. ๐ก 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) ๐ Real Outcomes Iโve Delivered โ Built full marketing data warehouse โ improved spend efficiency by 30%+ โ Fixed tracking & attribution โ recovered lost revenue visibility โ Automated reporting โ saved 20+ hrs/week for teams โ Delivered exec dashboards โ faster, data-backed decisions ๐ง 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 โ pipelines โ dashboards โ insights) - Strong communication + fast execution (no hand-holding needed) ๐ 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. ๐ Letโs Work If youโre looking for someone who can own your entire data stack and turn it into a revenue engine, letโs talk. Click โInviteโ and letโs discuss your use case ๐ ----- ๐ ๐๐๐ฌ๐ช๐ข๐ฅ๐๐ฆ 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
- Big Data
- Amazon Web Services
- Apache Airflow
- BigQuery
- Business Intelligence
- Python
- Google Tag Manager
- SQL
- Data Science
- Looker Studio
- Google Cloud Platform
- Artificial Intelligence
- Data Engineering
- ETL Pipeline
- Data Warehousing
- Data Visualization
- Tableau
- Generative AI
- AWS Lambda
- Amazon Redshift
Islamabad, Pakistan
๐ Hello, I'm Malik Salman. I specialize in helping businesses stand out by making smarter decisions through data analytics solutions. With over five years of experience and a strong focus on big data, data analysis, and interactive reporting, I bring a deep understanding of how to turn raw data into meaningful insights. My core strengths lie in data modeling and optimization, automation, and building intuitive self-service reporting solutions. ๐ง What I bring to the table: โ Expert-level database design, administration, and SQL programming โ End-to-end data extraction, ETL, and transformation workflows โ Comprehensive data preparation and cleansing โ High-performance data model design and optimization โ Advanced analytics with Power Pivot and DAX โ Dynamic, insightful dashboards in Power BI, Tableau, and Looker Studio โ Workflow automation using Power Automate โ Azure-based app development with Microsoft Power Apps โ Effective training and ongoing support to empower your team ๐ Data Analyst & Business Intelligence Expertise: As a data analyst, visualization specialist and Excel expert, I build scalable dashboards in Power BI, Tableau, Excel. My data analyst approach ensures efficient workflows for every data analyst project. ๐ง Advanced DAX formulas and Excel functions for KPI dashboards ๐ Power BI and Tableau dashboards for operations, sales, finance visualization ๐ Expert visualization in Power BI, Excel, and Tableau highlighting trends ๐ Dashboard development from Excel data extraction to Power BI publishing ๐ Data analyst transformation with Power Query, Excel, Tableau Prep ๐ Database connections (SQL, Snowflake) for Power BI dashboards ๐ API integration (REST, GraphQL) for Power BI and Tableau dashboards ๐ Integrated visualization with Power BI, Excel, Tableau, DAX ๐ Data Analyst Use Cases & Dashboard Experience: ๐งฉ Sales Analytics - Data analyst visualization using Power BI, Excel, Tableau ๐ผ Finance Dashboards - Excel models with Power BI visualization dashboards ๐ฏ Marketing Analytics - Campaign visualization with Tableau and Power BI ๐ก Executive Dashboards - Data analyst visualization for C-level in Power BI, Excel ๐ Geo Analytics - Location visualization using Power BI and Tableau dashboards ๐ Operations Dashboards - Data analyst KPI tracking with Excel and Power BI ๐ Customer Retention - Track metrics with Power BI, Excel, and Tableau dashboards ๐ CRM Analytics - Data analyst, Salesforce data in Tableau, Excel, Power BI ๐ง Data Analyst Tools & Skills โ Data analyst using Power BI, Tableau, Excel, DAX, and Power Query for dashboards โ Data analyst mastery of Excel functions, pivot tables for dashboard techniques โ Tableau Desktop, Prep, and visualization for dashboards โ Excel data analysis with Power Pivot for enhanced dashboards โ DAX formulas for Power BI dashboards and Excel reports โ SQL, PostgreSQL database integration for Power BI dashboards โ Snowflake, Azure SQL connections for data analysis dashboards โ REST API, GraphQL integration for Excel and Power BI dashboards โ JSON data processing for Tableau and Power BI visualization โ Data analyst pipeline automation for Excel and Power BI dashboards โ Excel financial modeling with Power BI visualization โ Tableau calculations for dynamic dashboards โ DAX time intelligence for Power BI dashboards and Excel reports ๐ฌ Let's Connect ๐ฌ Click INVITE to start your project with an expert data analyst. ๐ Ready to discuss your data analyst needs for Power BI, Excel, Tableau, DAX.
- Tableau
- Business Intelligence
- Database
- Data Science
- Sentiment Analysis
- Data Analysis
- Microsoft Power BI
- Data Analysis Expressions
- Exploratory Data Analysis
- Looker
- Google Analytics
- Data Visualization
- Microsoft Power BI Data Visualization
- Azure DevOps
- Microsoft Dynamics CRM
Chicago, Illinois
I build custom data and AI solutions that fit your business. Not off-the-shelf tools that sort of work. Hands-on, direct communication, full accountability. Projects of any size. A lead-scoring model for Salesforce. Large-scale ETL with Snowflake, Airflow, and Python. Custom analytics applications that answer questions your team actually asks. When projects need more capacity, I tap a trusted network of developers and analysts I've worked with for years. What I build: - AI integrations (RAG, agents, document intelligence, local LLMs) - Data infrastructure (Snowflake, Airflow, dbt, Python) - Custom analytics applications - Cloud architecture (AWS, Azure, GCP) 8+ years. 100% Job Success. Top Rated Plus. Expert Vetted. If you want solutions built for how your business actually works, let's talk.
- Big Data
- Data Management
- Data Warehousing
- Analytics
- Data Visualization
- Snowflake
- Data Warehousing & ETL Software
- Microsoft Power BI
- Database Design
- Microsoft Azure
- AWS Development
- Apache Airflow
- AI Consulting
- AI Data Analytics
- Tableau
Kiev, Ukraine
Top 1% Upwork EXPERT-VETTED Data & Solution Architect. For over 15 years, I've worked with data, focusing the last 7 years as a Data Architect on managing high-load and big-data projects. I oversee systems that serve hundreds of millions of users and manage petabytes of data, along with overseeing infrastructure budgets of over 1.5 million USD each year. In the past 8 years on Upwork, I have completed more than 100 projects with a 100% Job Success Score and a 94%+ client return rate. SERVICES โ Data Engineering โ AI/ML โ AI Adoption & GenAI โ Predictive analytics โ High-load Data Scraping โ Data Integration - seamless connection through APIs โ Data Management โ ETL pipelines, Data Warehousing, Data Lakes, DataOps โ IT Infrastructure & Solution Architecture โ DevOps for Performance Optimization and Cost Reduction STACK & TECHNOLOGIES Core stack: Python Data Warehousing: Redshift | BigQuery | Snowflake | Teradata Data Lakes: Hadoop | Amazon S3 | Azure Data Lake Storage | Google Cloud Storage ETL Tools and Data Pipelines: Databricks Workflows | AWS Glue | Google Cloud Dataflow Big Data Processing: Spark | Hadoop | Google Dataflow | Dask | Amazon Kinesis | Kafka Databases: MySQL | PostgreSQL | Google BigQuery | MongoDB | DynamoDB | Redis Data Analysis and Machine Learning: Pandas | NumPy | Dask | SciPy | OpenCV | TensorFlow | PyTorch | Scikit-learn | XGBoost | Keras | spaCy GenAI: OpenAI GPT-3 | GPT-4 | Lama 2 | Llava | Bard | Mistral | Anthropic Visualization Tools and Libraries: Power BI | Looker | Quicksight | Grafana | Plotly | D3.js | Dash | Matplotlib DevOps and DataOps AWS | GCP | Azure | Docker | Kubernetes | Amazon ECS | Jenkins | GitLab CI/CD | GitHub CI/CD | Bitbucket Pipelines | Ansible | Terraform | CloudFormation | Prometheus | Grafana | Datadog | Zabbix | ELK Stack | Elasticsearch | Logstash | Kibana | Graylog Web Development Front-End: React.js | Redux | Redux-Saga Back-End: Python | Django Rest Framework | Flask We use the Agile approach to ensure great products delivery. As our benefits, we provide the guarantee, NDA signing, and post-service for delivered products. Want to work together? Iโd love to hear from you!
- Big Data
- Python
- Data Engineering
- ETL Pipeline
- Data Warehousing
- Data Lake
- Database Architecture
- Cloud Architecture
- Data Science
- DevOps
- Machine Learning
- Data Warehousing & ETL Software
- Artificial Intelligence
- Generative AI
- Solution Architecture
- Software Architecture
- Data Management
- Data Analysis
- Data Modeling
- Data Processing
Sfax, Tunisia
Your design deserves more than "good enough." It deserves pixel-perfect. Most developers build what works. I build what you pictured โ exact, fast, and ready to ship. I'm Ayman, a Top Rated Frontend Developer with a 100% Job Success Score and 17+ projects across Angular, React, and Next.js. I turn Figma designs into responsive, high-performance web apps clients are proud to launch. I engineer the decisions. AI helps me move faster. You get clean, thoughtful work โ delivered quicker. What I do best: Figma โ pixel-perfect code ยท responsive, mobile-first UIs ยท Angular & React + TypeScript ยท Tailwind & SCSS. Clients say: โญ "Goes the extra mile โ happy to keep working with him." โญ "A solid front-end developer. I'd recommend him without hesitation." โญ "Exceeded expectations โ clean, efficient, well-structured." $20K+ earned. 800+ hours. 100% satisfaction. Let's build something you're proud of.
- Front-End Development Framework
- Front-End Development
- Responsive Design
- Web Design
- HTML
- CSS
- JavaScript
- TypeScript
- Angular
- React
- Web Application
- HTML5
- SCSS
- Bootstrap
- Tailwind CSS
- AI Code Generator
Austin, Texas
๐ Expert-Vetted | 9+ years of experience | ๐ Full Time Availability | โ Verifiable projects | โ๏ธ 17,000+ Hours ๐ Winner of 2 Presidential Awards in the country of operations ๐ Member of Chamber of Commerce in the country of operations ๐ Work Recognition on TV shows and Blogs ๐ Regularly conducts IT Bootcamps in the country of operationsย Streamlining Business Operations Through Data Empowerment! 9+ years of experience | Automation Specialist | Data Driven Operations | Full Stack Web Development | Data Science | Data Management | AI/ML Implementations | Deep Learning Solutions | Process Optimization | Business Performance Enhancement ๐ Big Data: I help business owners and their teams unlock the true value of their historic data, aligning sales and marketing teams, as well as product teams with the demand and supply dynamics of both the market, and the company's product/service offerings. Big Data Tools Integration (e.g., Apache Hadoop, Apache Spark) ETL Processes Implementation (Extract, Transform, Load) Data Retention Policies Distributed Computing Hadoop Cluster Management Stream Processing Systems (e.g., Apache Kafka) NoSQL Databases (e.g., MongoDB, DynamoDB) ML Toolkits (e.g., TensorFlow) Lambda Architecture ๐ Data Science: I help build business owners and their teams robust predictive analytics systems to better align their budgeted with the actual figures at year end, thereby reducing the delta and increasing the likelihood of shareholder alignment, improving the company's going-concern assumption for the stakeholders, and thereby improving the share value in the long run. Selecting features, building and optimising classifiers using ML techniques. Data mining Data collection (Apache Kafka, Logstash) Third-Party Data Integration (e.g., APIs, Selenium, Postman) Data Integrity Verification Anomaly Detection Systems Query Language Proficiency (e.g., SQL, MySQL, MongoDB, BigQuery, PostgreSQL) Scripting Skills (e.g., Python) Statistical Analysis Effective Communication Skills ๐ Full Stack Technology: Frontend: HTML, CSS, JavaScript (ES6, ES7, Typescript), JavaScript frameworks (e.g., React, Next.js, Angular), GraphQL clients (e.g., Apollo), CSS preprocessors (e.g., SASS, LESS), JavaScript charting libraries (e.g., D3, Highcharts, Recharts), State management (e.g., Redux, Redux Saga, React Toolkit), RESTful APIs Backend: Node.js, Nest.js, Express, Python, Cloud Computing Platforms (AWS, Google Cloud, IBM Cloud) Testing frameworks (e.g., Jest, Mocha) CI/CD tools (e.g., GitHub Actions, Jenkins, AWS CodePipeline) Version control tools (e.g., Git) MERN Stack (React.js, Node.js, MongoDB, Express.js) CMS (WordPress, WooCommerce) Deployment: Docker, Heroku, AWS, Azure UI/UX Material Design, Figma, Miro, HTML5, CSS, JavaScript, XML AI and Machine Learning (AI Assistants and Chatbots, ML Models, Predictive Analytics, Sentiment Analysis, NLP, Audio/Video/Speech to Text) LLMs: GPT-4o, ChatGPT, Claude, Mistral, Perplexity. Frameworks: LangChain, RAG, Crew AI, Vercel AI SDK. Vector databases: Qdrant, Pinecone, Milvus. APIs: Open AI, Azure AI Search, Eleven Labs, Whisper/DeepGram Data Visualisation (Power BI, Tableau) Driven by a passion for innovation and backed by a strong foundation in cutting-edge technologies, I'm committed to propelling your business towards unparalleled success. Let's harness the power of tech to revolutionize your operations! ๐ก
- Machine Learning
- Data Science
- Data Analysis
- Artificial Intelligence
- BigQuery
- ETL Pipeline
- API Integration
- Data Integration
- Data Modeling
- MERN Stack
- Python
- Django
- Node.js
- Generative AI
- Data Engineering
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What Is Big Data?
While big data has become a trendy catchphrase, the good news is that there is real substance to it. With a little effort, even nontechnical people can understand that substance and start putting it to work for their companies.
Part of demystifying the trendy catchphrase โbig dataโ is understanding that youโre analyzing your business using techniques of statistical analysis, some of which have been around for 50 years or more.
What is fundamentally different about the 21st-century phenomenon of โbig dataโ is the computing power we can bring to bear. Advances in the sensors that collect data, the drives that store it, and the software and hardware to analyze it mean that we can efficiently analyze far more material than was feasible in earlier centuries.
Itโs no longer hard to create and store gigabytes of dataโthe challenge is to find something meaningful in all of that material. What makes analyzing the data such a rich source of business insights?
Big data is good at finding correlations but not at causality
A great place to start is with the distinction between โwhat you likeโ and โwhy you like itโโor what is technically called the difference between correlation and causality. These algorithms donโt know why you like what you like. But they have learned what you will like based on what youโve purchased before.
From a business perspective, thatโs OKโwhat matters far more than why. Knowing what you will like drives clicks and sales. Skilled data scientists have a host of statistical techniquesโsome new, some oldโfor analyzing information. Before you start working with a data scientist, however, thereโs an important question you need to ask first.
Whatโs the type of dataset you want to learn more about?
If you donโt ask this all-important question, you could get overwhelmed with raw data. Many executives feel pressure to just do something with big data, so they begin collecting without a clear goal in mind.
If you do โtrack everything,โ youโll still have to go through that data again once you figure out what youโre trying to do. And in the meantime, youโll be racking up software, hardware, and personnel costs.
A key takeaway? Donโt just rush in and start tracking everything. The best way to get started is to look at the types of problems people have successfully attacked with big data in order to see what you might accomplish in your business. Here are a few examples:
- Branding: Look at mentions of a product on Twitter in order to derive an analysis of โcustomer sentiment.โ By collecting mentions of your brand from Twitter, data scientists not only can tell how customers feel about it but also how strongly they feel about it. Data scientists can also then help you automate your responses: re-tweeting of positive comments, and prompt, private messages to unhappy customers.
- Market research: Analyze your past sales records to segment your customer base so that you can find and target like-minded clusters of people with carefully customized marketing campaigns.
- Operations: Analyze the geolocation data of your delivery drivers to optimize the most efficient routes in terms of gasoline usage and time. Data scientists can compare up-to-the-minute data about where your vans are on the road with historical data about what routes are congested with vehicles or require time-consuming left-hand turns across traffic.
- Production optimization: A large beverage company used data to find the optimal blend of different kinds of oranges, which have different costs, astringency, sweetness, and tartness, in order to maximize profit while maintaining quality standards.
- Research: A large hedge fund hired researchers to keep track of real-time news on 200 companies at a time. The team was spending so much time seeking data, like looking for company press releases, regulatory sites, SEC filings, and updates to company websites, that they couldnโt keep up with all of the changes. Data consultancy BrightPlanet put together an algorithm to search the Internet and compile information automatically, freeing up the team to focus on analyzing the findings.
Tips for analyzing big data
There are some unusual features of massive datasets that you should keep in mind.
1. The โmessinessโ of big data
You may be surprised by how much time your consultants are using on a stage of the project called โdata preparation.โ Donโt be. Because computers, databases, and algorithms have gotten so fast, getting large datasets, often disorganized and drawn from multiple sources, in a position to be analyzed is quite challenging. โ
Data scientists unabashedly describe their datasets as โmessy.โ (Thatโs really the technical term for it.) Imagine, for example, you tell a web-crawling algorithm to compile massive amounts of press releases, tweets, news reports, and government filings from different websites and in different formats. The results from the web-crawling algorithm are not going to consist of neat, well-organized rows in a spreadsheet or fields in a database.
This โunstructuredโ data will need to be โcleanedโ or made uniform in a way that algorithms can analyze. Thatโs why โdata preparationโ often takes so much time.
2. You donโt need to sample
Unlike the analog days of statistics, when you might have given a survey to 1,100 people to stand in for your entire customer base, computing power today means you can look at all the data. And using all the data instead of a sample can make an enormous difference.
3. โDataficationโ
Viktor Mayer-Schรถnberger and Kenneth Cukier coined the term โdatafication,โ meaning that inexpensive sensors, hardware, and data storage have made it possible to collect certain types of data that were impractical to track previously.
4. Data exhaust
Because storage and collection has gotten cheap, you can save the equivalent of data โjunkโ and perhaps find ways to use it. For example, Google receives a large amount of search queries with typos or misspelled words each day. The company has taken this โexhaustโ from its lucrative search engine business in order to not only improve search (โDid you mean ornithologist?โ) but also to build a powerful spell-checker.
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