Begin by manually exporting data from LinkedIn. If you are an admin of a LinkedIn page, navigate to the analytics section of your page. Use LinkedIn's reporting tools to download the available analytics data, such as page views, engagement metrics, and follower demographics, into a CSV file. Keep in mind that LinkedIn's data export capabilities are limited, so you may need to manually copy and paste some data points.

After extraction, review the CSV files to ensure all necessary data is included. Clean the data by removing unnecessary columns or rows that do not need to be transferred to ClickHouse. Standardize column headers, and format data consistently, ensuring dates, numbers, and text fields are uniform.

Install the ClickHouse client on your local machine or server by downloading it from the official ClickHouse website or using a package manager. The client provides a command-line interface to interact with your ClickHouse database, enabling data insertion and querying.

Use the ClickHouse client to connect to your ClickHouse server. Define a table schema that matches the structure of the data you exported from LinkedIn. Use the `CREATE TABLE` SQL command to create a new table. Specify appropriate data types for each column, such as `String`, `Date`, `Int32`, etc.

```sql
CREATE TABLE linkedin_data (
date Date,
page_views Int32,
engagements Int32,
followers Int32
) ENGINE = MergeTree()
ORDER BY date;
```

Convert your cleaned CSV data into a format compatible with SQL insertion. You can use a scripting language like Python or a simple text editor to format the data as a series of `INSERT INTO` statements. Ensure that the values are properly quoted and escaped.

```sql
INSERT INTO linkedin_data (date, page_views, engagements, followers) VALUES
('2023-10-01', 100, 25, 500),
('2023-10-02', 120, 30, 505);
```

Execute the `INSERT INTO` statements using the ClickHouse client. You can either run the SQL commands directly via the command line or save them in a `.sql` file and execute the file using the client. Ensure that there are no syntax errors and that the data types match the table schema to avoid insertion failures.

Once the data is loaded, verify its integrity by querying the ClickHouse database. Use simple `SELECT` statements to check that all rows have been inserted correctly and that the data matches the original LinkedIn files. Perform spot checks on the data to ensure accuracy.

```sql
SELECT * FROM linkedin_data LIMIT 10;
```

By following these steps, you can manually move data from LinkedIn pages to a ClickHouse warehouse without relying on third-party connectors, providing you with a custom solution tailored to your specific needs.