Multiplier currently pays out salaries to various members under our payroll. Payouts to members are recorded in a raw ingestion file. This is loaded into a Google Sheet for your reference. The Product department needs you to build a pipeline to transform this raw data into a clean, query-able format for their analytics. Notes on Data: This is raw ingestion data. You may encounter inconsistent date formats, nested JSON strings, or mixed currencies. amounts: This contains a JSON-like string representing various components of the payout (Salary, Tax, Bonus). Part 1: Architecture & Modeling Before writing code, verbalise a strategy for this pipeline: Target Schema: Design a Star Schema (or appropriate data model) that this data should be transformed into to best answer the business questions below. Ingestion Strategy: How would you handle this file if it arrived daily? (Consider duplicates, partitioning, etc.) Part 2: Implementation Choose ONE of the following options based on your preferred stack: Option A: Python/DataFrame (Pandas, Spark, Polars) Implement a transformation script that reads the raw CSV and outputs the answers. Option B: SQL / ELT (Postgres, Snowflake, BigQuery) Assume the raw CSV data has already been loaded into a staging table (raw_payments) where all columns are currently TEXT/VARCHAR type. Write the SQL query to transform this raw table into your target schema and answer the business questions. Business Questions to Answer: Total Payouts: What is the total amount disbursed per currency in May 2023? Currency Analysis: What is the average salary per currency by Department? Note: You do not need an FX table; treat each currency as a separate group. Data Cleaning: Flatten the amounts column so that salary, tax, and bonus are distinct columns (or rows, depending on your modeling choice in Part 1). Constraints: You do not need to connect to a real database. Output the final results to the console or a clean CSV.