diff --git a/generic_loader/load_sas.py b/generic_loader/load_sas.py
index 9e18a64..5d97c02 100644
--- a/generic_loader/load_sas.py
+++ b/generic_loader/load_sas.py
@@ -2471,12 +2471,6 @@ def _seconds_to_time(v: Any) -> Optional[dt.time]:
 _SAS_DATETIME_SAFE_S = 7_500_000_000
 _SAS_DATETIME_SAFE_D = 87_000
 
-# Number of non-null values :func:`_safe_object_to_datetime` peeks at to
-# decide which parse path to use for the whole chunk. Keeps format
-# detection to a bounded cost so a 1M-row chunk doesn't pay for a
-# full row-walk just to figure out what shape its dates are in.
-_DATETIME_FORMAT_SAMPLE = 16
-
 
 def _safe_numeric_to_datetime(
     series: pd.Series,
@@ -2596,74 +2590,42 @@ def _safe_object_to_datetime(
             numeric, unit="s", column_name=column_name, target_type=target_type,
         )
 
-    # Format sniff: peek at up to ``_DATETIME_FORMAT_SAMPLE`` non-null
-    # string values to decide the parse path for the WHOLE chunk. The
-    # previous version ran a full row-walk + up-to-20 vectorized
-    # ``pd.to_datetime(format=fmt)`` attempts per column per chunk; a
-    # single fat chunk (millions of rows × a few date columns) could
-    # pin a CPU for minutes. Sniffing first keeps the hot path to one
-    # O(n) pass.
-    non_null = coerced.dropna()
-    if not non_null.empty:
-        samples: List[str] = []
-        for v in non_null.head(_DATETIME_FORMAT_SAMPLE):
-            if isinstance(v, str):
-                samples.append(v.strip())
-            else:
-                # Mixed object column (e.g. already-parsed Timestamps +
-                # strings). Skip sniffing; let dateutil handle it.
-                samples = []
-                break
+    non_null_count = int(coerced.notna().sum())
+    if non_null_count:
+        # First pass: our regex-based ``DD-MON-YY`` parser. Cheap,
+        # locale-independent, covers the cases ``pd.to_datetime`` warns
+        # about. Always parse via the datetime-aware variant so a DATE
+        # target whose chunk happens to carry time components
+        # (``23-MAR-20 14:30:00``) still parses without warnings; the
+        # caller's ``.dt.date`` cast truncates the time, matching the
+        # existing datetime64-input branch.
+        parsed_py = coerced.map(
+            lambda v: _parse_flexible_datetime(v) if v is not None else None
+        )
+        parsed_ts = pd.to_datetime(parsed_py, errors="coerce")
+        if int(parsed_ts.notna().sum()) == non_null_count:
+            return parsed_ts
 
-        if samples:
-            # DD-MON-YY family: one pandas ``Series.map`` with our
-            # regex parser, then a single ``pd.to_datetime`` to land
-            # on ``datetime64[ns]``. ``pd.to_datetime(format=...)``
-            # has no ``%b``-with-locale-free semantics, so this is
-            # the vectorized win available for this format family.
-            if all(_DDMONYY_RE.match(s) for s in samples):
+        # Second pass: vectorized ``pd.to_datetime`` with each explicit
+        # format. One ``pd.to_datetime(format=fmt)`` call is O(n) in C;
+        # trying a handful of them still beats row-by-row dateutil on
+        # large chunks. Accept the first format that covers every
+        # non-null cell.
+        for fmt in _EXTRA_DATETIME_FORMATS + _EXTRA_DATE_FORMATS:
+            try:
                 with np.errstate(over="ignore", invalid="ignore", divide="ignore"):
-                    return pd.to_datetime(
-                        coerced.map(
-                            lambda v: _parse_flexible_datetime(v)
-                            if isinstance(v, str) else None
-                        ),
-                        errors="coerce",
-                    )
+                    candidate = pd.to_datetime(coerced, format=fmt, errors="coerce")
+            except (ValueError, TypeError):
+                continue
+            if int(candidate.notna().sum()) == non_null_count:
+                return candidate
 
-            # Numeric strptime shapes: pick the first format that
-            # parses every sample, then run ONE vectorized
-            # ``pd.to_datetime(format=fmt)`` over the full column.
-            # Bounded to the sample pass -- no 20×O(n) blow-up.
-            for fmt in _EXTRA_DATETIME_FORMATS + _EXTRA_DATE_FORMATS:
-                ok = True
-                for s in samples:
-                    try:
-                        dt.datetime.strptime(s, fmt)
-                    except ValueError:
-                        ok = False
-                        break
-                if ok:
-                    try:
-                        with np.errstate(over="ignore", invalid="ignore", divide="ignore"):
-                            return pd.to_datetime(
-                                coerced, format=fmt, errors="coerce",
-                            )
-                    except (ValueError, TypeError):
-                        continue
-
-    # Fallback: ``pd.to_datetime`` / dateutil. Handles shapes the
-    # sniffer missed (mixed formats within one column,
-    # already-parsed Timestamp/date objects sharing space with
-    # strings, ISO 8601 with offsets, etc.). Wrap in a warning
-    # filter because the unformatted path emits ``UserWarning:
-    # Could not infer format...`` once per chunk and we don't want
-    # the progress bar drowned.
+    # Final fallback: ``dateutil`` via ``pd.to_datetime``. Handles
+    # shapes our explicit list missed (rare edge cases, mixed formats
+    # within one column). Same safety net as the numeric path.
     try:
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", category=UserWarning)
-            with np.errstate(over="ignore", invalid="ignore", divide="ignore"):
-                return pd.to_datetime(coerced, errors="coerce")
+        with np.errstate(over="ignore", invalid="ignore", divide="ignore"):
+            return pd.to_datetime(coerced, errors="coerce")
     except (FloatingPointError, OverflowError, ValueError) as exc:
         tqdm.write(
             f"[error] {target_type} column {column_name!r}: "