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You will get Professional Data Cleaning & Preparation for Excel / CSV / Google Sheets
Project details
I developed a fast and scalable data cleaning and normalization pipeline capable of processing large datasets with complex formatting issues. The system handles inconsistent schemas, malformed JSON, missing values, and duplicates, delivering clean and reliable data ready for analytics.
Using Python, Dask, and Pandas, the pipeline applies parallelized transformations, dynamic cleaning rules, schema validation, and optimized Parquet output. It is fully configurable and easy to extend for new data sources or business logic.
The result is a robust solution that converts raw, messy data into high-quality, analytics-ready datasets for BI, forecasting, and data warehousing workflows.
Using Python, Dask, and Pandas, the pipeline applies parallelized transformations, dynamic cleaning rules, schema validation, and optimized Parquet output. It is fully configurable and easy to extend for new data sources or business logic.
The result is a robust solution that converts raw, messy data into high-quality, analytics-ready datasets for BI, forecasting, and data warehousing workflows.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$25
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | 1 | 2 | Unlimited |
Number of Sources Mined/Scraped | 1 | 2 | 3 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $40
Additional Source Mined/Scraped
(+ 1 Day)
+$15Frequently asked questions
About Adrian
Senior Data Engineer | Airflow, dbt, PostgreSQL | ETL & Data Warehouse
Lerma de Villada, Mexico - 9:19 am local time
I specialize in improving existing data platforms where pipelines are slow, fragile, difficult to maintain, or becoming a bottleneck for analytics.
I can help you with:
• Apache Airflow DAG development, troubleshooting and optimization
• ETL/ELT pipeline design and incremental data ingestion
• dbt architecture, models, testing and data quality
• PostgreSQL query optimization and performance troubleshooting
• Data warehouse architecture and dimensional modeling
• Python data pipelines and API/database integrations
• Schema evolution, validation and automated quality checks
• CI/CD and testing for data pipelines
Recent production work includes:
• Designing maintainable Landing → Refined → Serving data architectures
• Migrating legacy ETL workloads to modular Airflow + dbt pipelines
• Building incremental, full-refresh and date-range ingestion strategies
• Diagnosing slow PostgreSQL queries using EXPLAIN ANALYZE and indexing strategies
• Optimizing analytics workloads while validating result parity before migration
I focus on solving the root cause—not just patching the immediate failure.
I work especially well with teams that prefer asynchronous, written communication, clear technical documentation and Git-based workflows.
If you have an unreliable Airflow DAG, slow SQL query, difficult-to-maintain ETL pipeline or a data warehouse that needs restructuring, send me the problem and I’ll help you determine the best next step.
Steps for completing your project
After purchasing the project, send requirements so Adrian can start the project.
Delivery time starts when Adrian receives requirements from you.
Adrian works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Review & Data Assessment
I analyze the raw dataset, understand the data structure, identify quality issues, and document the cleaning requirements.
Data Cleaning Rule Definition
I define custom rules for normalization, formatting, schema alignment, JSON correction, deduplication, and missing-value handling.