Analytics Specialist and Data Visualizer
Make.com Level 5 & Zapier Expert: AI and Ops Automation
Does someone on your team lose a day or two building the sales report every week or month?
That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling.
I built them an automation on Make.com (http://Make.com) a few months ago. It has run on its own ever since, weekly and monthly:
Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together.
It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period.
It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds.
It reads a Google Sheet of store representatives and emails every one of them the report for their own store.
The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero.
Two things I would do the same way again:
Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores.
Send people their slice, not the whole report. One report for everyone gets skimmed by everyone.
The figures in the image are placeholders, the real ones stay with the client.
What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
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How do you automate a daily sales report from BigQuery to Slack with Make.com, when the data is 250,000 rows and the reader is not an analyst? Six decisions make or break it.
A direct-to-consumer brand's daily sales report took one to two days to compile, which meant Monday's numbers arrived on Wednesday. A 7 AM Make.com scenario now pages the data out of BigQuery, computes the KPIs and posts a plain-English summary to Slack and email before anyone opens a laptop.
The six decisions:
1. Page, do not pull. A day can be up to 250,000 rows. The scenario pages through BigQuery in chunks and aggregates as it goes, so no single step holds the whole table.
2. Compute the KPIs in the scenario, not in the prompt. Revenue, orders, average order value, top products and day-on-day change are arithmetic. The model only writes the sentences.
3. Give the model numbers, never the rows. A short structured summary goes to OpenAI with a fixed template; the output is two paragraphs a founder reads in thirty seconds.
4. Check before you post. If a KPI is null, if the row count is far below the trailing average, if a query failed, the scenario alerts the owner instead of posting a confident wrong report.
5. Post where people already look. Slack channel at 7 AM plus an email copy for the people not in Slack. No dashboard to remember to open.
6. Keep a paper trail. Every run logs the query window, row count, KPIs and the text it posted, so a number can be traced back a month later.
The diagram shows the run: schedule, paged query, KPI step, summary, checks, Slack and email. The full case study is on my profile. The same scenario shape works with Shopify, Stripe, a CRM or a Sheet as the source.
Prem Patel, Nex Automations. Make.com Level 5 certified, official Make partner, Zapier Certified Expert.
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How do you show a client three website directions on one call without drowning in tabs?
This week we stopped juggling. We built a presenter page with Claude Code and ran the whole call from one tab.
The problem it solves: on a design call you open a tab per concept, resize the window to "show mobile", scroll hunting for the section you meant, and the client loses track of which version is which. The work was fine. The way we showed it wasn't.
What it does, all in one tab:
Direction tabs across the top. The real site runs inside a browser frame, so scrolling, hover states, animations and video all work live.
Desktop or Phone. Phone is a true 390 px view. Desktop renders the real 1440 px layout scaled to fit, so you see the actual design, not a squeezed one.
Full screen in one click, made for screen sharing.
Highlight buttons that jump the site straight to the section you are talking about: the opening, the work grid, the film.
A short page list (home, one project and contact), so the walkthrough has a beginning, middle and end.
The guardrail designers will appreciate: only 4 or 5 pages of each site open. The landing page, one full project and the key pages. Every other link shows "This page comes with the full build" instead of opening. You pitch the real thing without handing over the build before there is an agreement.
It travels too. Packed as a zip, it runs from a double-click with no server and no install, so our partner can run the same demo on his own calls.
Why not just send a staging link? It opens every page, and on a call the client wanders off on their own. This keeps everyone on the same screen.
What I would tell any designer pitching websites: show the site moving. Scroll, hover and motion are exactly what a PDF can't show.
How do you present website concepts today: a PDF, a Figma prototype or a live link?
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Full-Stack Developer | React, Next.js, Node.js & Python
I designed and built Distill IQ, a full-stack retail pricing intelligence platform for a US-based liquor business. The application processes and presents large-scale pricing data through interactive product surveys, competitor comparison analytics, raw data exploration, and user/access management. I worked across the React frontend, Python backend, APIs, BigQuery data layer, business logic, and UI components to build the platform from the ground up.
Key features:
Pricing Survey: 5,200+ SKUs, 110+ brands, search and advanced filtering
Competitor Analytics: Compare products across markets with average, median, mode, price ranges, and price gaps
Market Analytics: Interactive price distribution, historical trends, and competitor comparison
Raw Data Explorer: BigQuery-backed data exploration with filtering, pagination, and export
User Management: User accounts, roles, permissions, market access, and catalog management
Reusable UI: Tables, filters, cards, charts, forms, and responsive components
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Manufacturing Quality & Operations Platform
I designed and developed the frontend for a production-grade manufacturing quality and operations platform used within a multibillion-dollar global consumer goods enterprise. The application brings quality workflows, production reporting, SKU management, plant-level analytics, and role-based operations into a single platform.
I worked across the Next.js application architecture, UI/UX implementation, API integrations, authentication, state management, reporting workflows, and reusable component system.
Key areas
- Operations & Reporting: Adoption, SKU Plant Summary, Plan vs Actual, PCP Tracker, PCRO and CRQS reports
- Quality Workflows: Product/SKU management, barcode scanning, product -verification and multi-step SKU creation
- LQA Operations: Pending SKU workflows, employee/site selection, product details, barcode capture and validation
- Shift Supervisor: Employee selection and role-specific workflows
- Analytics: KPI cards, donut/bar charts, trends, filters and deep-dive analytics
- Admin: Site management, user profile, role-based access and protected routes
- Reusable UI: Tables, charts, cards, modals, forms, tabs, filters, dropdowns and navigation components
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Hi, I designed and developed Quality Station, a web application for managing and analysing manufacturing quality checks. The product brings together quality dashboards, product management, inspection sessions, AI-based analysis, defect detection, user management, and activity tracking into a single interface.
The UI was designed in Figma and implemented as a functional React/Next.js application, with a reusable component system, responsive layouts, interactive charts, data tables, filters, modals, forms, and multi-step workflows.
Key areas I worked on include:
- Analytics dashboards and quality monitoring
- AI performance and defect analysis
- Quality sessions with product/barcode verification
- Weight, barcode, and OCR analysis
- Live image and analysis views
- Product management and configurations
- User roles and access management
- Activity tracking
- Reusable tables, charts, forms, filters, and UI components
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AI Data Assistant Inside Tableau
I built an AI-powered chatbot for Tableau that allows users to interact with their existing dashboards and data through natural-language questions.
Instead of manually navigating through multiple Tableau workbooks to find a specific metric or insight, users can ask the assistant what they need and receive relevant information from the available Tableau data and dashboards.
The solution adds a conversational AI layer directly to the existing BI workflow, helping users explore their data and find the right dashboard or report without changing how their Tableau environment is structured.
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Senior Data Analyst | Business Intelligence Consultant
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Performance Marketer | Meta & Google Ads | AI Automation
Performance Marketer | Meta & Google Ads | AI Automation
Revenue Leak Radar: AI Analyst That Caught Broken Tracking on Day 1
📸 Real build, real screenshots, not mock-ups.
I built an AI analyst that checks a store's GA4 data every morning, spots revenue drops, funnel leaks and broken tracking, and explains each issue in plain English with a cost, an action and an owner.
The result: when revenue showed $0 but 19 customers had actually paid, the radar flagged it as a tracking problem on the first day, not a sales crash.
What it does:
• GA4 → BigQuery: 10 business metrics every day
• Compares each day to the same weekday over 28 days
• Catches orders recorded without revenue
• Gemini writes the diagnosis, action and owner
• Sends a visual report and an alert email
• "Ask your data" box: type a question, it writes and runs the SQL
Tools: Google Apps Script, BigQuery, GA4, Gemini API, Google Sheets
Cost: $0 (free tiers only)
Tested on Google's public GA4 demo dataset (Google Merchandise Store).
Full case study: https://claude.ai/artifact/KjLpN5RTwNLKTt3Ng77ssV
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Data visualization engineer, Data Analyst, BI Expert
National Accident Statistics Dashboard in Google Looker Studio:
I built an interactive Looker Studio dashboard using Google Sheets as the data source to visualize national accident statistics. The report analyzes accidents by region, time, severity, and demographics. A scheduled data re:fresh every 15 minutes keeps the dashboard updated in real time. As a Data Visualization Expert, I combined automation and design to deliver a clean, insightful, and dynamic Looker Studio dashboard for data-driven decision-making.
🎉 💯 🎉
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Product Associate
Hi everyone,
so its my day 1 here on contra, who am I?
just a normie 19 year old in his first year of CS major trying to figure things out,
so I have won around 7 Hackathons and few Product competitions? including wins at various IITs and IIMs and those wins made me realize one thing for sure- "I AM NOT MADE FOR CLASSIC 9 TO 5" and what made me realize that? I love interacting and building things together , i don't like those classy cubicle jobs, i learn by interacting and I am trying to discover what i want, being very honest i really am confused about what I am gonna do but one thing that I am sure about is I am gonna do something and figure things out by trying every shit I can and getting my hands dirty , at the same point I want to connect with you all and learn as much as i can from you guys(and obv always open to build something together :) ) so if you wanna connect, am just a DM away , will keep posting on daily basis ,trying to get some interactiveness here as well😔✋🏻.
will see you all tomorrow after completing another day of productivity :) .
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Growth Manager with 8+ years experience of AI in Growth
New to Contra
Growth Manager with 8+ years experience of AI in Growth
Enterprise Marketing Analytics & Attribution — West Pharmaceutical Services
Built analytics and measurement systems connecting digital marketing, customer behavior, CRM, advertising, and commercial outcomes for a global enterprise. Worked across GA4, GTM, BigQuery, SQL, Python, Power BI, advertising platforms, customer identity resolution, attribution, and executive reporting. Programs contributed to major improvements in digital revenue, sales efficiency, onboarding performance, retention economics, and visibility into marketing ROI.
Skills: GA4, GTM, BigQuery, SQL, Power BI, Attribution, Marketing Analytics, CRM
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Zero-to-20K User Growth — AI Product
Helped grow an AI product from zero to approximately 20,000 users without relying on paid acquisition. The work combined positioning, organic distribution, product-led growth, experimentation, audience development, analytics, and rapid iteration to identify repeatable acquisition loops.
Skills: Product-Led Growth, Organic Growth, AI Marketing, Growth Experiments, Analytics
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Building the Growth Engine for MinionArts
Built and operated MinionArts' early growth and distribution engine across organic acquisition, founder-led sales, partnerships, outbound, creators, content, and performance marketing. The work included ICP development, messaging, campaign experimentation, lead generation, sales outreach, analytics, attribution, and converting product capabilities into commercially relevant use cases for brands and studios.
Skills: Growth Strategy, Demand Generation, GTM, Marketing Automation, Founder-Led Sales
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MinionArts — AI-Native Creative Production Platform
Co-building MinionArts, an AI-native creative production platform for brands, agencies, and studios producing high-volume visual content. Worked across GTM, positioning, sales, partnerships, distribution, pricing, product strategy, and customer acquisition while helping develop workflows spanning script, storyboard, characters, scenes, voice, video generation, and final creative output. MinionArts grew to thousands of users through largely organic and founder-led distribution.
Skills: AI Strategy, GTM, Product Marketing, Growth, B2B Sales, Generative AI
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