You may try the following which uses explode to split the list of Employee Ids into different rows before joining them and using collect_list to aggregate the entries into a list.

Using spark sql:

NB. Ensure Employees is available as a table/view eg EmployeeData.createOrReplaceTempView("Employees")

WITH dept_employees AS (
    SELECT
        DeptId,
        DeptName,
        explode(EmployeeIDs)
    FROM
        vw_TestView
)
SELECT
    d.DeptId,
    d.DeptName,
    collect_list(e.EmpID) as EmployeeIDs,
    collect_list(e.EmpName) as EmployeeNames
FROM
    dept_employees d
INNER JOIN
    Employees e ON d.col=e.EmpID
GROUP BY
    d.Deptid,
    d.DeptName

or using pyspark api:

from pyspark.sql import functions as F

output_df = (
    departMentData.select(
        F.col("DeptId"),
        F.col("DeptName"),
        F.explode("EmployeeIDs")
    )
    .alias("d")
    .join(
        EmployeeData.alias("e"),
        F.col("d.col")==F.col("e.EmpID"),
        "inner"
    )
    .groupBy("d.DeptId","d.DeptName")
    .agg(
        F.collect_list("e.EmpID").alias("EmployeeIDs"),
        F.collect_list("e.EmpName").alias("EmployeeNames")
    )
)

Let me know if this works for you.