diff --git a/etl/grapher_helpers.py b/etl/grapher_helpers.py
index 058598ee9f2..413ebfaa66f 100644
--- a/etl/grapher_helpers.py
+++ b/etl/grapher_helpers.py
@@ -36,7 +36,9 @@
 )
 
 # this might work too pd.api.types.is_integer_dtype(col)
-INT_TYPES = tuple({f"{n}{b}" for n in ("int", "Int", "uint", "UInt") for b in ("8", "16", "32", "64")})
+INT_TYPES = tuple(
+    {f"{n}{b}{p}" for n in ("int", "Int", "uint", "UInt") for b in ("8", "16", "32", "64") for p in ("", "[pyarrow]")}
+)
 
 
 def as_table(df: pd.DataFrame, table: catalog.Table) -> catalog.Table:
diff --git a/lib/repack/owid/repack/__init__.py b/lib/repack/owid/repack/__init__.py
index 1aa6461a306..92eaeba4a3d 100644
--- a/lib/repack/owid/repack/__init__.py
+++ b/lib/repack/owid/repack/__init__.py
@@ -157,18 +157,18 @@ def series_eq(lhs: pd.Series, rhs: pd.Series, cast: Any, rtol: float = 1e-5, ato
 def _safe_dtype(dtype: Any) -> str:
     """Determine the appropriate dtype string based on pandas dtype."""
     if pd.api.types.is_integer_dtype(dtype):
-        return "Int64"
+        return "int64[pyarrow]"
     elif pd.api.types.is_float_dtype(dtype):
-        return "Float64"
+        return "float64[pyarrow]"
     elif isinstance(dtype, pd.CategoricalDtype):
-        return "string[python]"
+        return "string[pyarrow]"
     else:
         return dtype
 
 
 def to_safe_types(t: pd.DataFrame) -> pd.DataFrame:
-    """Convert numeric columns to Float64 and Int64 and categorical
-    columns to string[python]. This can significantly increase memory usage."""
+    """Convert numeric columns to float64[pyarrow] and int64[pyarrow] and categorical
+    columns to string[pyarrow]."""
     t = t.astype({col: _safe_dtype(t[col].dtype) for col in t.columns})
 
     if isinstance(t.index, pd.MultiIndex):