Excel Data Cleaning & Structuring Expert Needed

Objective:
To clean, standardize, and structure existing Excel files containing production, downtime, rejection, and maintenance records from our steel tube manufacturing plant. The goal is to prepare the data for integration into analytics tools like Power BI, SQL databases, or ML systems for preventive maintenance and operational insights.

Scope of Work:
You will be required to:

Review & Analyze Excel files (may vary in format and structure) used for:

Daily production logs (Pilger, Drawbench, Piercing, etc.)

Downtime reports

Rejection logs

Tool/mandrel/plug breakage records

Maintenance task logs (Preventive & Corrective)

Clean & Normalize Data:

Standardize date/time formats

Remove or flag duplicate or inconsistent records

Ensure consistent headers and column structures across files

Uniformize text entries (e.g., machine names, shift labels, downtime reasons)

Structure the Data into Master Tables:
Convert all raw files into clean, structured master files:

production_logs.csv

downtime_logs.csv

rejection_logs.csv

maintenance_logs.csv

machine_master.csv
Each file should have consistent, labeled columns (a template will be provided or co-developed).

(Optional): Use Power Query or Python (Pandas) to automate repeat cleaning tasks if required.

Documentation & Comments:

Provide a short documentation or readme with:

Field definitions for each file

Explanation of cleaning done

Any assumptions or flagged issues

Skills Required:
Proficient in Microsoft Excel (incl. Power Query)

Experience with data cleaning and structuring

(Optional) Experience with Python (pandas) or SQL

Strong attention to detail

Deliverables:
Final cleaned and structured Excel/CSV files.

Folder with:

Raw → Cleaned conversion script (if scripted)

Documentation (brief comments, assumptions, issues)

(Optional) Excel or Power BI summary dashboard with key indicators.