Hello, I am

Francisco Meneke

Business Data Analyst | Data Engineer

Who am I ?

A Data Analyst / Engineer

I’m a Data Analyst with a passion for transforming raw data into actionable insights. With expertise in Power BI, SQL, and Python, I specialize in data visualization, business intelligence, and data-driven decision-making. My work focuses on uncovering trends, optimizing processes, and helping businesses make smarter decisions.
Feel free to check out my work and download my CV below!

Personal Info

  • Birthdate : 30th September
  • Email : menekefrancisco@gmail.com
  • Phone : + (234) 703-725-6644
  • Address : Oshodi-Isolo, Lagos State.

My Expertise

Data Visualization

DAX | Power BI | Excel


SQL

SQL Server | Postgres


Programming

Python .


My Resume

Experience

Marketing Performance Analyst

StackwisR Limited (2024 - Present)

Marketing Analyst

Business Analyst Consultant

Pairview Solutions Limited (2023 - 2024)

Business Analyst
Data Analyst

D.O.Z Superstores (2022 - 2023)

Business Analyst

Education

B.Sc. Zoology

Faculty of Biological Sciences (2014 - 2018)

Nnamdi Azikiwe University, Awka

Skills

HTML5 | CSS3 | Bootstrap
Power Automate | Zapier | Make
Python
SQL
SPSS
Power BI/Excel

Languages

English
Igbo
Portugese

I Am Available For FreeLance

My Portfolio

Customer Churn Report


Inventory Management Report


Inventory Management Queries


-- Link to download the WideWorldImportersDW database
-- https://github.com/Microsoft/sql-server-samples/releases/download/wide-world-importers-v1.0/WideWorldImportersDW-Full.bak

-- Fact Transaction Table
-- This query retrieves sales transaction data from the Fact.Order table.
-- It includes details such as order dates, stock items, quantities, customer invoices, and delivery status.
-- The CASE statement categorizes deliveries as 'On Time', 'Late', or 'Not Delivered Yet'.

SELECT FO.[Order Date Key],
        FO.[Picked Date Key],
        FO.[WWI Order ID],
        FO.[Stock Item Key],
        FO.Quantity,
        FO.[Total Including Tax],
        FO.[WWI Backorder ID],
        SI.OrderID,
        SI.InvoiceID,
        FS.[Customer Key],
        FS.[Invoice Date Key],
        FS.[Delivery Date Key],
        DATEDIFF(DAY, FO.[Order Date Key], FS.[Delivery Date Key]) AS OrderToDeliveryLeadTime,
        SO.ExpectedDeliveryDate,
        CASE
            WHEN SO.ExpectedDeliveryDate >= FS.[Delivery Date Key] THEN 'On Time Delivery'
            WHEN FS.[Delivery Date Key] IS NULL THEN 'Not Delivered Yet'
            ELSE 'Late Delivery'
        END AS [Delivery Status]
FROM [Fact].[Order] FO 
LEFT JOIN [WideWorldImporters].[Sales].[Invoices] SI
    ON FO.[WWI Order ID] = SI.OrderID
LEFT JOIN [Fact].[Sale] FS
    ON SI.InvoiceID = FS.[WWI Invoice ID] 
    AND FO.[Stock Item Key] = FS.[Stock Item Key]
LEFT JOIN [WideWorldImporters].[Sales].[Orders] SO
    ON FO.[WWI Order ID] = SO.OrderID
ORDER BY FO.[WWI Order ID];

-- Products Table
-- Retrieves details about stock items, including pricing, tax rate, and weight.
-- Excludes the default stock item key (0) to ensure only valid items are shown.

SELECT [Stock Item Key],
        [WWI Stock Item ID],
        [Stock Item],
        [Lead Time Days],
        [Quantity Per Outer],
        [Is Chiller Stock],
        [Tax Rate],
        [Unit Price],
        [Recommended Retail Price],
        [Typical Weight Per Unit]
FROM [Dimension].[Stock Item]
WHERE [Stock Item Key] != 0;

-- Stock Holding Table
-- Retrieves stock holding details from the Fact.StockHolding table.
-- This table tracks inventory levels over time.

SELECT * 
FROM [Fact].[Stock Holding];

-- Supplier Performance
-- Evaluates supplier reliability by counting complete and incomplete purchases.
-- A complete purchase means the received quantity matches or exceeds the ordered quantity.

SELECT s.Supplier, 
        COUNT(CASE WHEN p.[Received Outers] >= p.[Ordered Outers] THEN 'Complete' END) AS [Complete Purchase], 
        COUNT(CASE WHEN p.[Received Outers] < p.[Ordered Outers] THEN 'Incomplete' END) AS [Incomplete Purchase],
        COUNT(*) AS Total
FROM [Fact].[Purchase] p
LEFT JOIN [Dimension].[Supplier] s
    ON p.[Supplier Key] = s.[Supplier Key]
GROUP BY s.Supplier;

-- Supplier Invoice Duration
-- Calculates the time taken to finalize supplier invoices from the transaction date.
-- Helps assess supplier payment efficiency and compare it with expected payment terms.
-- Uses explicit database schema references for clarity and to access information outside the current working database.

SELECT s.SupplierName,
        st.TransactionDate AS [Invoice Date],
        st.FinalizationDate AS [Payment Date],
        DATEDIFF(DAY, st.TransactionDate, st.FinalizationDate) AS [Duration before Payment],
        po.PaymentDays
FROM [WideWorldImporters].[Purchasing].[SupplierTransactions] st
LEFT JOIN [WideWorldImporters].[Purchasing].[PurchaseOrders] po
ON st.PurchaseOrderID = po.PurchaseOrderID
LEFT JOIN [WideWorldImporters].[Application].[TransactionTypes] tt
ON st.TransactionTypeID = tt.TransactionTypeID
LEFT JOIN [WideWorldImporters].[Purchasing].[Suppliers] s
ON st.SupplierID = s.SupplierID
WHERE po.OrderDate IS NOT NULL AND st.FinalizationDate IS NOT NULL;
                                    
                                
Download Query

Data Extraction and Loading with Python

This project showcases the first version of a Python-based data extraction tool designed to automate the retrieval of structured data from SQL Server and save it as CSV files for analysis. The script leverages PyODBC for database connectivity and Pandas for efficient data processing, streamlining data extraction workflows.

Key Features & Skills Demonstrated:

  • ✅ Secure database connection using PyODBC
  • ✅ Querying and extracting data from SQL Server
  • ✅ Processing and saving datasets with Pandas
  • ✅ Automating data export for analytical workflows

Future Enhancements:

  • 🚀 Next Release: Expanding support for multiple database types beyond SQL Server
  • 🚀 Final Version: Transforming the notebook into an interactive script that prompts users for required details (e.g., server name, database name) and executes the extraction process automatically—eliminating the need for manual code interaction

Send a message

Get in touch

Phone :
+ (234) 703-725-6644
Address :
Oshodi-Isolo, Lagos, Nigeria
Email :
menekefrancisco@gmail.com