diff --git a/pipeline_pgp/data/PGP x D4G- Exported Vaccine Data.xlsx b/pipeline_pgp/data/PGP x D4G- Exported Vaccine Data.xlsx
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Binary files /dev/null and b/pipeline_pgp/data/PGP x D4G- Exported Vaccine Data.xlsx differ
diff --git a/pipeline_pgp/pgp_ingest.py b/pipeline_pgp/pgp_ingest.py
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+"""
+PGP Excel ingestion script
+
+Reads all sheets from the PGP export file and loads them into PostgreSQL.
+Tables are created automatically in the raw schema.
+
+Each ingestion adds a snapshot_date so historical data is preserved.
+If the script runs multiple times the same day, duplicates are avoided.
+"""
+
+import pandas as pd
+import psycopg2
+from psycopg2 import sql
+from datetime import date
+import re
+
+# =========================
+# Configuration
+# =========================
+
+FILE_PATH = "data/PGP x D4G- Exported Vaccine Data.xlsx"
+
+DB_CONFIG = {
+    "host": "localhost",
+    "port": "5432",   # Change docker postgres port
+    "dbname": "eu_fact_force",
+    "user": "eu_fact_force",
+    "password": "eu_fact_force"
+}
+
+RAW_SCHEMA = "raw"
+
+
+# =========================
+# Helper functions
+# =========================
+
+def clean_name(name):
+    """
+    Clean sheet and column names so they are valid SQL identifiers.
+    """
+    name = name.lower()
+    name = name.replace("%", "percent")
+    name = re.sub(r"[^\w]+", "_", name)
+    return name.strip("_")
+
+
+def create_schema(cursor):
+    """
+    Ensure schemas exist.
+    """
+    cursor.execute("CREATE SCHEMA IF NOT EXISTS raw;")
+    cursor.execute("CREATE SCHEMA IF NOT EXISTS analytics;")
+
+
+def create_table_if_not_exists(cursor, table_name, columns):
+    """
+    Create table dynamically from dataframe columns.
+    """
+
+    column_defs = []
+
+    for col in columns:
+        column_defs.append(sql.SQL("{} TEXT").format(sql.Identifier(col)))
+
+    column_defs.append(sql.SQL("snapshot_date DATE"))
+
+    query = sql.SQL("""
+        CREATE TABLE IF NOT EXISTS {}.{} (
+            id SERIAL PRIMARY KEY,
+            {}
+        )
+    """).format(
+        sql.Identifier(RAW_SCHEMA),
+        sql.Identifier(table_name),
+        sql.SQL(", ").join(column_defs)
+    )
+
+    cursor.execute(query)
+
+
+def insert_dataframe(cursor, table_name, df):
+    """
+    Insert dataframe rows into PostgreSQL.
+    """
+
+    cols = list(df.columns)
+    cols.append("snapshot_date")
+
+    insert_query = sql.SQL("""
+        INSERT INTO {}.{} ({})
+        VALUES ({})
+    """).format(
+        sql.Identifier(RAW_SCHEMA),
+        sql.Identifier(table_name),
+        sql.SQL(", ").join(map(sql.Identifier, cols)),
+        sql.SQL(", ").join(sql.Placeholder() * len(cols))
+    )
+
+    snapshot = date.today()
+
+    for _, row in df.iterrows():
+        values = [str(v) if pd.notna(v) else None for v in row.tolist()]
+        values.append(snapshot)
+        cursor.execute(insert_query, values)
+
+
+# =========================
+# Main ingestion process
+# =========================
+
+def ingest_excel():
+
+    print("Reading Excel file...")
+
+    xls = pd.ExcelFile(FILE_PATH)
+    sheets = xls.sheet_names
+
+    conn = psycopg2.connect(**DB_CONFIG)
+    cursor = conn.cursor()
+
+    create_schema(cursor)
+
+    for sheet in sheets:
+
+        print(f"Ingesting sheet: {sheet}")
+
+        df = pd.read_excel(FILE_PATH, sheet_name=sheet)
+
+        # clean column names
+        df.columns = [clean_name(c) for c in df.columns]
+
+        table_name = clean_name(sheet)
+
+        create_table_if_not_exists(cursor, table_name, df.columns)
+
+        insert_dataframe(cursor, table_name, df)
+
+        conn.commit()
+
+    cursor.close()
+    conn.close()
+
+    print("Ingestion complete.")
+
+
+# =========================
+
+if __name__ == "__main__":
+    ingest_excel()
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