Muhammad Mudassir Raza

🛠 Skills

🎓 Certifications & Credentials

• Databricks Lakehouse Fundamentals — Databricks, 2023
• Certified AWS Cloud Practitioner — Coursera, 2023
• Certified AWS Data Analytics — Udemy, 2023
• Certified Data Engineer — KarachidotAI, 2023
• Certified Data Engineer — Emeritus USA, 2023
• Certified in Python — Google (Coursera), 2023

🏅 Recognition

• "Unveiling the Stars" — NASA Astronauts Hackathon, HiCounselor, 2023

🧩 Selected Projects

Architecture-led projects spanning cloud data pipelines, analytics engineering, AI automation, streaming, and data modeling.

🧠 Mindify

An empathetic chatbot that takes a user's text or voice input, detects the underlying emotion, retrieves grounded CBT/psychology insights from a vector database, and uses Gemini 2.0 Flash to craft a caring, practical response — complete with coping tips, next steps, and a cited book insight.

⚡ Real-Time Crypto Data Pipeline

A real-time pipeline that scrapes live crypto prices, streams them through a Kafka producer/consumer pair, cleans and lands them as JSON in an S3 bucket, then auto-ingests into Snowflake via Snowpipe the moment new files land — no manual loading step. Every service (Zookeeper, Kafka broker, producer, consumer) runs as its own Docker container.

📊 Crypto Market Analytics Pipeline

Pulls live coin data from the CoinGecko API into BigQuery, models it into a proper star schema with a SCD Type 2 table that tracks each coin's market-cap rank history over time, then feeds a daily summary view into two places at once: an LLM that writes newsletter-style market commentary, and a Streamlit dashboard that shows both the charts and that commentary side by side.

🌀 Data Orchestration with Airflow

A daily-scheduled Airflow DAG that downloads a public NYC taxi dataset, converts it from CSV to Parquet for efficient storage and querying, uploads it to Google Cloud Storage, then loads it straight into BigQuery — each step a separate, retry-able task chained by dependencies. Airflow itself (webserver, scheduler, Postgres metadata DB) runs entirely through Docker.

📈 Stock Market Real-Time Pipeline (Kafka + Glue/Athena + Snowflake)

Streams historical stock index data row-by-row through Kafka to simulate a live feed, lands it as JSON in S3, then makes it queryable two different ways: an AWS Glue crawler builds a schema catalog so Athena can query S3 directly with no load step, while Snowflake ingests the same files automatically through Snowpipe for warehouse-side analysis — comparing a serverless query path against a managed warehouse path on the same data.

🤖 Personal Assistant — n8n AI Agent

An AI agent built as an n8n workflow: a webhook receives a request, hands it to a Gemini-powered agent that keeps conversational memory across turns, and the agent decides which of six connected tool groups to call — Gmail, web search, Calendar, Docs, Tasks, or Sheets — before the result is sent back through the same webhook.

💵 Nealytics — Multi-Source Revenue Data Pipeline

A Google Cloud Function that pulls revenue data from ThriveCart, Stripe (across multiple accounts, fetched in parallel), Shopify, and PayPal, and loads it all into BigQuery — with incremental loading for ThriveCart (only new rows past the last synced date) and parallel fetching for Stripe to keep runtime down. Every run posts a pass/fail summary to Slack, so failures get flagged immediately instead of going unnoticed.

🛡️ Insightly CRM → OneDrive Export Pipeline

A scheduled Azure Logic App triggers five separate Azure Functions, each responsible for a slice of Insightly CRM data — quotes, organisations, opportunities, equipment/invoices/users, tasks, and opportunity stages. Every function pulls its entity via the Insightly API, exports it to CSV, then authenticates with Microsoft Graph (MSAL client-credentials flow) to upload the file straight into a shared OneDrive/SharePoint folder — splitting the work across functions keeps each run well under Azure's execution time limits.

📉 GA4 → BigQuery Reporting Pipeline

A scheduled Cloud Function that pulls the last 10 days of sessions, events, and key events from a Google Analytics 4 property (by landing page and date), then reconciles it into BigQuery using a delete-then-append pattern — deleting the overlapping date range first so re-runs never create duplicates. Every GA4 call and BigQuery write retries with exponential backoff, and a Slack alert fires the moment a step exhausts its retries.

🔎 Search Console → BigQuery Pipeline

A single Cloud Function that runs two independent sync paths against the same Search Console property: one pulls performance grouped by search query, the other by landing page. The query-level path uses the same delete-then-append pattern as the GA4 pipeline to stay idempotent on re-runs; the page-level path currently appends only. Both land in their own BigQuery table, validated afterwards with clicks/impressions/position roll-up queries.

📞 CallRail → BigQuery Pipeline

Syncs call tracking data from CallRail into BigQuery without ever re-processing what's already there: it reads the latest timestamp already in BigQuery as a watermark, splits the gap up to today into monthly chunks, fetches all of them in parallel (7 threads via joblib), then diffs the fetched IDs against what's already in BigQuery before uploading — so only genuinely new calls, users, and form submissions ever get written.

📍 Google Business Profile → BigQuery Pipeline

Pulls performance metrics for six Google Business Profile store locations at once, running two sub-pipelines in the same function call: daily metrics for the last 8 days, and monthly search-keyword data for the last month. Each pipeline fetches all six locations in parallel (7 workers), then reconciles into BigQuery with the same delete-then-append pattern used across the other Google-data pipelines, so re-runs stay duplicate-free.

🏆 Wincher SEO Rank-Tracking Pipeline

Tracks keyword rankings, keyword groups, and competitor positions from Wincher for over a dozen client sites (Michigan Auto Law, IHOP, Outback, Qdoba, Ferguson Roofing, and more), each run as its own scheduled Cloud Function built on one shared Wincher client module. Every run covers a rolling 3-month window split into single-day intervals, fetched in parallel (5 threads) with automatic retry on transient API errors, then truncates and reloads that date range in BigQuery so the numbers stay accurate as Wincher's own data gets revised.

📱 Facebook & Instagram Ads Data Pipeline

Pulls marketing and organic data from Meta's platform through five independent modules — Page insights, post performance, ad performance, ad creative details, and Instagram user insights — each loading into its own BigQuery table. A same-day-run guard checks whether a table already has today's data before pulling again, avoiding wasted API calls when the function gets triggered more than once in a day.

🏛️ CFPB Complaints Pipeline — Airflow + Streamlit

A daily Airflow DAG recurses through every U.S. state to pull consumer financial complaint data from the CFPB's public API, transforms it, and pushes it into Google Sheets — which a Streamlit dashboard then reads directly to give stakeholders a state-by-state, always-current view of complaint trends without anyone touching a spreadsheet by hand.

🐙 Scraping 1M+ GitHub Repositories on GCP

Paginates through GitHub's public repository listing and fetches over a million repos, enriching each with follower counts, languages, and stargazers through concurrent worker threads with automatic retry on failed requests. The resulting dataset then feeds an exploratory analysis notebook that surfaces trends across languages, audiences, and user attributes — turning raw API output into a dataset someone can actually draw conclusions from.

⭐ Northwind OLTP → OLAP Star Schema (PySpark SCD2)

Redesigns the classic Northwind OLTP database into a proper star schema: a fact table grained at order/order-detail level with foreign keys to denormalized Employee, Customer, and Product dimensions, plus a Date dimension. MySQL procedures migrate the data from OLTP to OLAP, and PySpark implements a working Slowly Changing Dimension (Type 2) on the Employee dimension — validated against both an insert case and an update case to prove history is tracked correctly.

🚢 Nested JSON Streaming — Kafka + PySpark

Reads nested Titanic-dataset JSON messages off a Dockerized Kafka topic, applies an explicit schema to work with the nested fields through PySpark's API, then flattens everything to a single level. From there it drops duplicate rows, enforces that key columns are never null, removes fields that aren't needed downstream, fixes numeric types, and writes the cleaned result out as line-delimited JSON.