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You will get accurate Data Entry, Cleaning, and Final Report with Pivot Tables
Project details
Need clean, reliable data fast? I offer meticulous data collection, entry, and cleaning services to deliver spreadsheets ready for immediate reporting. As a beginner committed to earning a 5-star reputation, my primary focus is 100% accuracy and structured data formatting. I use Excel and Google Sheets to transform your raw sources (URLs, documents) into a perfectly organized, useful dataset.
Data Tool
Microsoft ExcelWhat's included
| Service Tiers |
Starter
$10
|
Standard
$15
|
Advanced
$20
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 6 days |
Number of Revisions | 1 | 2 | 3 |
Number of Pages Mined/Scraped | 5 | 10 | 15 |
Number of Sources Mined/Scraped | 1 | 2 | 3 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$2
Source Verification
+$5About Dilnoza
Azure Data Engineer & Analytics Architect | PySpark, dbt & Modern Data
Tashkent, Uzbekistan - 10:34 am local time
I help enterprises turn messy, fragmented data into highly scalable, automated Data Warehouses and Lakehouses using modern data engineering practices.
With an MSc in Business Intelligence & Analytics and hands-on experience processing massive datasets (2+ Billion records / 100GB+ using PySpark & Azure Synapse), I build the heavy-duty data engines that make fast and reliable analytics possible.
🔹 Why work with me? (The Communication Advantage)
Before transitioning into robust data engineering, I earned a Master's in TESOL and worked as an IT English educator. What does this mean for you? You will never deal with language barriers, poor documentation, or confusing tech jargon. I bridge the gap between technical infrastructure and business goals, explaining complex architectures in crystal-clear English.
🔹 What I bring to your business:
• Modern ELT & Data Warehousing: Designing highly optimized Star/Galaxy schemas and modern Lakehouses (Azure Databricks, Snowflake).
• Big Data & Orchestration: Building resilient data pipelines extracting data from complex APIs, JSON, XML to clean storage using Apache Spark (PySpark), Azure Data Factory (ADF), and Apache Airflow.
• Analytics Engineering (dbt): Transforming and modeling data within the warehouse to ensure 100% data quality and readiness for BI tools (Power BI, Tableau).
🔹 My Core Tech Stack:
• Python, PySpark, SQL (T-SQL, PL/SQL), Golang
• Azure (Synapse, Data Factory, Data Lake Gen2, Databricks)
• dbt (Data Build Tool), Airflow, Snowflake, PostgreSQL
• Power BI, Tableau, Advanced Data Modeling
Whether you need to fix a broken ETL pipeline, migrate legacy databases to a modern cloud setup, or build a scalable data architecture from scratch—I’d love to help.
📩 Let's build a data system you can trust. Send me a message and let's hop on a quick 10-minute call to discuss your architecture!
Steps for completing your project
After purchasing the project, send requirements so Dilnoza can start the project.
Delivery time starts when Dilnoza receives requirements from you.
Dilnoza works on your project following the steps below.
Revisions may occur after the delivery date.
Data Collection & Initial Entry/Cleaning
I carefully gather data from the provided sources (URLs, PDFs, documents) and input it into the designated format (Excel/Sheets). I run cleaning processes (removing duplicates, correcting formatting errors, standardizing entries).
Reporting and Summarization
Data is organized into pivot tables or a summary report for quick business insights.