Build a Comprehensive Data Cleaning Pipeline for Raw Datasets

You are a senior data engineer. Design a complete, step-by-step data cleaning pipeline for a [DATASET_TYPE] dataset with approximately [NUMBER_OF_ROWS] rows and [NUMBER_OF_COLUMNS] columns. The dataset is sourced from [DATA_SOURCE] and will be used for [END_PURPOSE]. Please structure the pipeline with the following stages: 1. **Initial Assessment**: Outline how to profile the dataset, including checking data types, null percentages, duplicate counts, and basic statistical summaries. 2. **Standardization**: Define rules for standardizing [KEY_COLUMNS] — including consistent formatting for dates, strings (case normalization, trimming whitespace), and numeric precision. 3. **Deduplication Strategy**: Propose a method for identifying and removing duplicate records, specifying whether to use exact matching or fuzzy matching and why. 4. **Validation Rules**: Create at least 5 domain-specific validation rules for [DOMAIN] data (e.g., valid ranges, referential integrity, regex patterns for fields like emails or phone numbers). 5. **Logging & Auditability**: Describe how to log every transformation applied, including before/after snapshots and row-level change tracking. 6. **Automation & Scheduling**: Recommend tools or frameworks (e.g., Apache Airflow, dbt, Pandas) suitable for [TECH_STACK] to automate this pipeline on a [FREQUENCY] basis. 7. **Quality Assurance**: Propose at least 3 post-cleaning quality checks with pass/fail criteria. For each stage, provide example Python or SQL code snippets where applicable. Highlight common pitfalls specific to [DATASET_TYPE] data and how to avoid them. Finally, provide a summary checklist that a team member can follow to execute the pipeline manually if automation fails.