diff --git a/CHANGES.rst b/CHANGES.rst
index 9ebe78b840..0b6da3affb 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -13,6 +13,12 @@ associations
 
 - Constraint added to Asn_Lv3Coron to remove background exposures [#5781]
 
+resample
+--------
+
+- Make inverse variance ``weight_type="ivm"`` the default weighting scheme for
+  multiple exposures resampled into a single output. [#5738]
+
 
 1.0.0 (2021-02-22)
 ==================
diff --git a/JenkinsfileRT b/JenkinsfileRT
index 00e92c7029..57e31b37e0 100644
--- a/JenkinsfileRT
+++ b/JenkinsfileRT
@@ -82,7 +82,6 @@ bc0.build_cmds = [
     "pip install -e .[test,ephem]",
     "pip install pytest-xdist pytest-sugar",
     "pip freeze",
-    'echo "CRDS_CONTEXT = $(crds list --contexts $CRDS_CONTEXT --mappings | grep pmap)"',
 ]
 bc0.build_cmds = PipInject(env.OVERRIDE_REQUIREMENTS) + bc0.build_cmds
 bc0.test_cmds = [
diff --git a/JenkinsfileRT_dev b/JenkinsfileRT_dev
index 998f437958..3a7a70890a 100644
--- a/JenkinsfileRT_dev
+++ b/JenkinsfileRT_dev
@@ -83,7 +83,6 @@ bc0.build_cmds = [
     "pip install -e .[test,ephem]",
     "pip install pytest-xdist pytest-sugar",
     "pip freeze",
-    'echo "CRDS_CONTEXT = $(crds list --contexts $CRDS_CONTEXT --mappings | grep pmap)"',
 ]
 bc0.build_cmds = PipInject(env.OVERRIDE_REQUIREMENTS) + bc0.build_cmds
 bc0.test_cmds = [
diff --git a/jwst/datamodels/schemas/core.schema.yaml b/jwst/datamodels/schemas/core.schema.yaml
index a1b9215805..910e7bc683 100644
--- a/jwst/datamodels/schemas/core.schema.yaml
+++ b/jwst/datamodels/schemas/core.schema.yaml
@@ -2324,7 +2324,7 @@ properties:
           weight_type:
             title: Type of drizzle weighting to use in resampling input
             type: string
-            enum: [exptime, error]
+            enum: [exptime, ivm]
             fits_keyword: RESWHT
       background:
         title: Background information
diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py
index d59edc2aab..6ff796cc33 100644
--- a/jwst/outlier_detection/outlier_detection.py
+++ b/jwst/outlier_detection/outlier_detection.py
@@ -110,7 +110,7 @@ def _convert_inputs(self):
                                               dq=self.inputs.dq[i])
                 image.meta = self.inputs.meta
                 image.wht = build_driz_weight(image,
-                                              weight_type='exptime',
+                                              weight_type='ivm',
                                               good_bits=bits)
                 self.input_models.append(image)
             self.converted = True
@@ -202,7 +202,7 @@ def do_detection(self):
             for i in range(len(self.input_models)):
                 drizzled_models[i].wht = build_driz_weight(
                     self.input_models[i],
-                    weight_type='exptime',
+                    weight_type='ivm',
                     good_bits=pars['good_bits'])
 
         # Initialize intermediate products used in the outlier detection
diff --git a/jwst/outlier_detection/outlier_detection_scaled.py b/jwst/outlier_detection/outlier_detection_scaled.py
index 48c605e20c..496d19eb31 100644
--- a/jwst/outlier_detection/outlier_detection_scaled.py
+++ b/jwst/outlier_detection/outlier_detection_scaled.py
@@ -106,7 +106,7 @@ def do_detection(self):
         for image in self.input_models:
             image.wht = resample_utils.build_driz_weight(
                 image,
-                weight_type='exptime',
+                weight_type='ivm',
                 good_bits=pars['good_bits']
             )
 
diff --git a/jwst/outlier_detection/outlier_detection_scaled_step.py b/jwst/outlier_detection/outlier_detection_scaled_step.py
index 4d5a8c100f..9e900ac94f 100755
--- a/jwst/outlier_detection/outlier_detection_scaled_step.py
+++ b/jwst/outlier_detection/outlier_detection_scaled_step.py
@@ -19,7 +19,7 @@ class OutlierDetectionScaledStep(Step):
     """
 
     spec = """
-        weight_type = option('exptime','error',None,default='exptime')
+        weight_type = option('ivm','exptime',default='ivm')
         pixfrac = float(default=1.0)
         kernel = string(default='square') # drizzle kernel
         fillval = string(default='INDEF')
diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py
index 4a6633de17..b2a7d30c16 100644
--- a/jwst/outlier_detection/outlier_detection_spec.py
+++ b/jwst/outlier_detection/outlier_detection_spec.py
@@ -85,7 +85,7 @@ def do_detection(self):
             for i in range(len(self.input_models)):
                 drizzled_models[i].wht = resample_utils.build_driz_weight(
                                             self.input_models[i],
-                                            weight_type='exptime',
+                                            weight_type='ivm',
                                             good_bits=pars['good_bits'])
 
         # Initialize intermediate products used in the outlier detection
diff --git a/jwst/outlier_detection/outlier_detection_stack_step.py b/jwst/outlier_detection/outlier_detection_stack_step.py
index 07d22b1bf0..a379b61538 100644
--- a/jwst/outlier_detection/outlier_detection_stack_step.py
+++ b/jwst/outlier_detection/outlier_detection_stack_step.py
@@ -28,7 +28,7 @@ class OutlierDetectionStackStep(Step):
     """
 
     spec = """
-        weight_type = option('exptime','error',None,default='exptime')
+        weight_type = option('ivm','exptime',default='ivm')
         pixfrac = float(default=1.0)
         kernel = string(default='square') # drizzle kernel
         fillval = string(default='INDEF')
diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py
index 61de9bbf23..7e1a83f4fc 100644
--- a/jwst/outlier_detection/outlier_detection_step.py
+++ b/jwst/outlier_detection/outlier_detection_step.py
@@ -46,7 +46,7 @@ class OutlierDetectionStep(Step):
     # by the various versions of the outlier_detection algorithms, and each
     # version will pick and choose what they need while ignoring the rest.
     spec = """
-        weight_type = option('exptime','error',None,default='exptime')
+        weight_type = option('ivm','exptime',default='ivm')
         pixfrac = float(default=1.0)
         kernel = string(default='square') # drizzle kernel
         fillval = string(default='INDEF')
diff --git a/jwst/resample/gwcs_drizzle.py b/jwst/resample/gwcs_drizzle.py
index 34e2461983..a8b8494339 100644
--- a/jwst/resample/gwcs_drizzle.py
+++ b/jwst/resample/gwcs_drizzle.py
@@ -1,7 +1,6 @@
 import numpy as np
 
 from drizzle import util
-from drizzle import doblot
 from drizzle import cdrizzle
 from . import resample_utils
 
@@ -13,9 +12,8 @@ class GWCSDrizzle:
     """
     Combine images using the drizzle algorithm
     """
-    def __init__(self, product, outwcs=None, single=False,
-                 wt_scl="exptime", pixfrac=1.0, kernel="square",
-                 fillval="INDEF"):
+    def __init__(self, product, outwcs=None, single=False, wt_scl=None,
+                 pixfrac=1.0, kernel="square", fillval="INDEF"):
         """
         Create a new Drizzle output object and set the drizzle parameters.
 
@@ -33,10 +31,10 @@ def __init__(self, product, outwcs=None, single=False,
             provided, the WCS is taken from product.
 
         wt_scl : str, optional
-            How each input image should be scaled. The choices are `exptime`
-            which scales each image by its exposure time, `expsq` which scales
-            each image by the exposure time squared, or an empty string, which
-            allows each input image to be scaled individually.
+            How each input image should be scaled. The choices are `exptime`,
+            which scales each image by its exposure time, or `expsq`, which scales
+            each image by the exposure time squared.  If not set, then each
+            input image is scaled by its own weight map.
 
         pixfrac : float, optional
             The fraction of a pixel that the pixel flux is confined to. The
@@ -64,7 +62,10 @@ def __init__(self, product, outwcs=None, single=False,
         self.outexptime = 0.0
         self.uniqid = 0
 
-        self.wt_scl = wt_scl
+        if wt_scl is None:
+            self.wt_scl = ""
+        else:
+            self.wt_scl = wt_scl
         self.kernel = kernel
         self.fillval = fillval
         self.pixfrac = pixfrac
@@ -90,11 +91,7 @@ def __init__(self, product, outwcs=None, single=False,
             self.outcon = np.reshape(self.outcon, (1,
                                      self.outcon.shape[0],
                                      self.outcon.shape[1]))
-
-        elif self.outcon.ndim == 3:
-            pass
-
-        else:
+        elif self.outcon.ndim != 3:
             raise ValueError("Drizzle context image has wrong dimensions: \
                 {0}".format(product))
 
@@ -102,11 +99,6 @@ def __init__(self, product, outwcs=None, single=False,
         if not self.outwcs:
             raise ValueError("Either an existing file or wcs must be supplied")
 
-        if util.is_blank(self.wt_scl):
-            self.wt_scl = ''
-        elif self.wt_scl != "exptime" and self.wt_scl != "expsq":
-            raise ValueError("Illegal value for wt_scl: %s" % self.wt_scl)
-
         if out_units == "counts":
             np.divide(self.outsci, self.outexptime, self.outsci)
         elif out_units != "cps":
@@ -199,34 +191,6 @@ def add_image(self, insci, inwcs, inwht=None, xmin=0, xmax=0, ymin=0, ymax=0,
                   xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
                   pixfrac=self.pixfrac, kernel=self.kernel, fillval=self.fillval)
 
-    def blot_image(self, blotwcs, interp='poly5', sinscl=1.0):
-        """
-        Resample the output image using an input world coordinate system.
-
-        Parameters
-        ----------
-
-        blotwcs : wcs
-            The world coordinate system to resample on.
-
-        interp : str, optional
-            The type of interpolation used in the resampling. The
-            possible values are "nearest" (nearest neighbor interpolation),
-            "linear" (bilinear interpolation), "poly3" (cubic polynomial
-            interpolation), "poly5" (quintic polynomial interpolation),
-            "sinc" (sinc interpolation), "lan3" (3rd order Lanczos
-            interpolation), and "lan5" (5th order Lanczos interpolation).
-
-        sincscl : float, optional
-            The scaling factor for sinc interpolation.
-        """
-
-        util.set_pscale(blotwcs)
-        self.outsci = doblot.doblot(self.outsci, self.outwcs, blotwcs,
-                                    1.0, interp=interp, sinscl=sinscl)
-
-        self.outwcs = blotwcs
-
     def increment_id(self):
         """
         Increment the id count and add a plane to the context image if needed
diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py
index 772c032625..74fb5bf082 100644
--- a/jwst/resample/resample.py
+++ b/jwst/resample/resample.py
@@ -41,7 +41,7 @@ class ResampleData:
          (eventually) a record of metadata from all input models.
     """
     def __init__(self, input_models, output=None, single=False, blendheaders=True,
-        pixfrac=1.0, kernel="square", fillval="INDEF", weight_type="exptime",
+        pixfrac=1.0, kernel="square", fillval="INDEF", weight_type="ivm",
         good_bits=0, pscale_ratio=1.0, **kwargs):
         """
         Parameters
@@ -147,8 +147,7 @@ def do_drizzle(self):
                                             single=self.single,
                                             pixfrac=self.pixfrac,
                                             kernel=self.kernel,
-                                            fillval=self.fillval,
-                                            wt_scl=self.weight_type)
+                                            fillval=self.fillval)
 
             for n, img_exp in enumerate(exposure):
                 # Make a copy as this is operated on in-place below
@@ -165,7 +164,7 @@ def do_drizzle(self):
                     weight_type=self.weight_type,
                     good_bits=self.good_bits)
                 driz.add_image(img.data, img.meta.wcs, inwht=inwht,
-                    expin=img.meta.exposure.exposure_time)
+                               expin=img.meta.exposure.exposure_time)
 
             # Update some basic exposure time values based on all the inputs
             output_model.meta.exposure.exposure_time = texptime
diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py
index 5d38afd4d8..6a84fc2a05 100644
--- a/jwst/resample/resample_spec.py
+++ b/jwst/resample/resample_spec.py
@@ -46,7 +46,7 @@ class ResampleSpecData:
 
     def __init__(self, input_models, output=None, single=False,
         blendheaders=False, pixfrac=1.0, kernel="square", fillval=0,
-        weight_type="exptime", good_bits=0, pscale_ratio=1.0, **kwargs):
+        weight_type="ivm", good_bits=0, pscale_ratio=1.0, **kwargs):
         """
         Parameters
         ----------
diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py
index a5ac0b655b..1692dc4418 100755
--- a/jwst/resample/resample_step.py
+++ b/jwst/resample/resample_step.py
@@ -34,7 +34,7 @@ class ResampleStep(Step):
         pixfrac = float(default=1.0)
         kernel = string(default='square')
         fillval = string(default='INDEF')
-        weight_type = option('exptime', default='exptime')
+        weight_type = option('ivm', 'exptime', default='ivm')
         pixel_scale_ratio = float(default=1.0) # Ratio of input to output pixel scale
         single = boolean(default=False)
         blendheaders = boolean(default=True)
@@ -68,8 +68,8 @@ def process(self, input):
             self.log.info('Drizpars reference file: {}'.format(ref_filename))
             kwargs = self.get_drizpars(ref_filename, input_models)
         else:
-            # Deal with NIRSpec which currently has no default drizpars reffile
-            self.log.info("No NIRSpec DIRZPARS reffile")
+            # If there is no drizpars reffile
+            self.log.info("No DIRZPARS reffile")
             kwargs = self._set_spec_defaults()
 
         kwargs['allowed_memory'] = self.allowed_memory
@@ -113,7 +113,6 @@ def get_drizpars(self, ref_filename, input_models):
         pixfrac = float(default=None)
         kernel = string(default=None)
         fillval = string(default=None)
-        weight_type = option('exptime', default=None)
 
         Once the defaults are set from the reference file, if the user has
         used a resample.cfg file or run ResampleStep using command line args,
@@ -148,15 +147,13 @@ def get_drizpars(self, ref_filename, input_models):
             self.log.error("No row found in %s matching input data.", ref_filename)
             raise ValueError
 
-        # Define the keys to pull from drizpars reffile table.  Note the
-        # step param 'weight_type' is 'wht_type' in the FITS binary table.
+        # Define the keys to pull from drizpars reffile table.
         # All values should be None unless the user set them on the command
         # line or in the call to the step
         drizpars = dict(
             pixfrac=self.pixfrac,
             kernel=self.kernel,
             fillval=self.fillval,
-            wht_type=self.weight_type,
             pscale_ratio=self.pixel_scale_ratio,
             )
 
@@ -177,9 +174,6 @@ def get_drizpars(self, ref_filename, input_models):
 
         all_drizpars = {**reffile_drizpars, **user_drizpars}
 
-        # Convert the 'wht_type' key to a 'weight_type' key
-        all_drizpars['weight_type'] = all_drizpars.pop('wht_type')
-
         kwargs = dict(
             good_bits=GOOD_BITS,
             single=self.single,
@@ -188,12 +182,6 @@ def get_drizpars(self, ref_filename, input_models):
 
         kwargs.update(all_drizpars)
 
-        if 'wht_type' in kwargs:
-            raise DeprecationWarning('`wht_type` config keyword has changed ' +
-                'to `weight_type`; ' +
-                'please update calls to ResampleStep and resample.cfg files')
-            kwargs.pop('wht_type')
-
         for k,v in kwargs.items():
             self.log.debug('   {}={}'.format(k, v))
 
@@ -220,7 +208,7 @@ def _set_spec_defaults(self):
         if kwargs['fillval'] is None:
             kwargs['fillval'] = 'INDEF'
         if kwargs['weight_type'] is None:
-            kwargs['weight_type'] = 'exptime'
+            kwargs['weight_type'] = 'ivm'
         kwargs['pscale_ratio'] = self.pixel_scale_ratio
         kwargs.pop('pixel_scale_ratio')
 
diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py
index 4795d5bfd5..6559c680db 100644
--- a/jwst/resample/resample_utils.py
+++ b/jwst/resample/resample_utils.py
@@ -6,7 +6,7 @@
 from astropy.modeling import Model
 from astropy.modeling.models import Mapping
 from astropy import units as u
-from gwcs import WCS, wcstools
+import gwcs
 
 from jwst.assign_wcs.util import wcs_from_footprints, wcs_bbox_from_shape
 from jwst.datamodels.dqflags import interpret_bit_flags
@@ -72,7 +72,7 @@ def calc_gwcs_pixmap(in_wcs, out_wcs, shape=None):
         bb = in_wcs.bounding_box
         log.debug("Bounding box from WCS: {}".format(in_wcs.bounding_box))
 
-    grid = wcstools.grid_from_bounding_box(bb)
+    grid = gwcs.wcstools.grid_from_bounding_box(bb)
     pixmap = np.dstack(reproject(in_wcs, out_wcs)(grid[0], grid[1]))
 
     return pixmap
@@ -99,7 +99,7 @@ def reproject(wcs1, wcs2):
 
     if isinstance(wcs1, fitswcs.WCS):
         forward_transform = wcs1.all_pix2world
-    elif isinstance(wcs1, WCS):
+    elif isinstance(wcs1, gwcs.WCS):
         forward_transform = wcs1.forward_transform
     elif issubclass(wcs1, Model):
         forward_transform = wcs1
@@ -109,7 +109,7 @@ def reproject(wcs1, wcs2):
 
     if isinstance(wcs2, fitswcs.WCS):
         backward_transform = wcs2.all_world2pix
-    elif isinstance(wcs2, WCS):
+    elif isinstance(wcs2, gwcs.WCS):
         if not is_sky_like(wcs1.output_frame):
             # nirspec lamps: simplify backward transformation by omitting the msa_x (it's constant)
             # and just using the wavelength lookup table [1] and linear msa_y transformation [2]
@@ -140,26 +140,35 @@ def _reproject(x, y):
 
 
 def build_driz_weight(model, weight_type=None, good_bits=None):
-    """ Create input weighting image
+    """Create a weight map for use by drizzle
     """
     dqmask = build_mask(model.dq, good_bits)
-    exptime = model.meta.exposure.exposure_time
-
-    if weight_type == 'error':
-        err_model = np.nan_to_num(model.err)
-        inwht = (exptime / err_model)**2 * dqmask
-        log.debug("DEBUG weight mask: {} {}".format(type(inwht), np.sum(inwht)))
-    # elif weight_type == 'ivm':
-    #     _inwht = img.buildIVMmask(chip._chip,dqarr,pix_ratio)
+
+    if weight_type == 'ivm':
+        if model.hasattr("var_rnoise"):
+            with np.errstate(divide="ignore", invalid="ignore"):
+                inv_variance = model.var_rnoise**-1
+            inv_variance[~np.isfinite(inv_variance)] = 1
+        else:
+            warnings.warn("var_rnoise array not available.  Setting drizzle weight map to 1",
+                RuntimeWarning)
+            inv_variance = 1.0
+        result = inv_variance * dqmask
     elif weight_type == 'exptime':
-        inwht = exptime * dqmask
+        exptime = model.meta.exposure.exposure_time
+        result = exptime * dqmask
     else:
-        inwht = np.ones(model.data.shape, dtype=model.data.dtype)
-    return inwht
+        warnings.warn("weight_type set to None.  Setting drizzle weight map to 1",
+            RuntimeWarning)
+        result = np.ones(model.data.shape, dtype=model.data.dtype)
+
+    return result.astype(np.float32)
 
 
 def build_mask(dqarr, bitvalue):
-    """ Builds a bit-mask from an input DQ array and a bitvalue flag
+    """Build a bit mask from an input DQ array and a bitvalue flag
+
+    In the returned bit mask, 1 is good, 0 is bad
     """
     bitvalue = interpret_bit_flags(bitvalue)
 
@@ -167,6 +176,7 @@ def build_mask(dqarr, bitvalue):
         return (np.ones(dqarr.shape, dtype=np.uint8))
     return np.logical_not(np.bitwise_and(dqarr, ~bitvalue)).astype(np.uint8)
 
+
 def is_sky_like(frame):
     # Differentiate between sky-like and cartesian frames
     return u.Unit("deg") in frame.unit or u.Unit("arcsec") in frame.unit
diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py
index 8a4cd2633a..1a514ed25e 100644
--- a/jwst/resample/tests/test_resample_step.py
+++ b/jwst/resample/tests/test_resample_step.py
@@ -3,14 +3,16 @@
 import numpy as np
 import pytest
 
-from jwst.datamodels import ImageModel
+from jwst.datamodels import ImageModel, ModelContainer
 from jwst.assign_wcs import AssignWcsStep
 from jwst.extract_2d import Extract2dStep
 from jwst.resample import ResampleSpecStep, ResampleStep
 
 @pytest.fixture
 def nirspec_rate():
-    shape = (2048, 2048)
+    ysize = 2048
+    xsize = 2048
+    shape = (ysize, xsize)
     im = ImageModel(shape)
     im.meta.wcsinfo = {
         'dec_ref': 40,
@@ -65,8 +67,10 @@ def nirspec_rate():
 def miri_rate():
     xsize = 72
     ysize = 416
-    im = ImageModel((ysize, xsize))
-    im.data += 5.
+    shape = (ysize, xsize)
+    im = ImageModel(shape)
+    im.data += 5
+    im.var_rnoise = np.random.random(shape)
     im.meta.wcsinfo = {
         'dec_ref': 40,
         'ra_ref': 100,
@@ -116,8 +120,8 @@ def miri_rate():
 def nircam_rate():
     xsize = 204
     ysize = 204
-    im = ImageModel((ysize, xsize))
-    im.data += 5.
+    shape = (ysize, xsize)
+    im = ImageModel(shape)
     im.meta.wcsinfo = {
         'ctype1': 'RA---TAN',
         'ctype2': 'DEC--TAN',
@@ -222,6 +226,7 @@ def test_pixel_scale_ratio_spec(miri_rate, ratio):
 @pytest.mark.parametrize("ratio", [0.5, 0.7, 1.0])
 def test_pixel_scale_ratio_imaging(nircam_rate, ratio):
     im = AssignWcsStep.call(nircam_rate, sip_approx=False)
+    im.data += 5
     result1 = ResampleStep.call(im)
     result2 = ResampleStep.call(im, pixel_scale_ratio=ratio)
 
@@ -237,6 +242,37 @@ def test_pixel_scale_ratio_imaging(nircam_rate, ratio):
     assert_allclose(area1 * ratio**2, area2, rtol=1e-6)
 
 
+def test_weight_type(nircam_rate, _jail):
+    """Check that weight_type of exptime and ivm work"""
+    im1 = AssignWcsStep.call(nircam_rate, sip_approx=False)
+    im2 = im1.copy()
+    im3 = im1.copy()
+    im1.data += 10
+    im2.data += 5
+    im3.data += 5
+    im1.var_rnoise += (1 / 10)
+    im2.var_rnoise += (1 / 5)
+    im3.var_rnoise += (1 / 5)
+    im2.meta.observation.sequence_id = "2"
+    im3.meta.observation.sequence_id = "3"
+
+    c = ModelContainer([im1, im2, im3])
+    assert len(c.group_names) == 3
+
+    result1 = ResampleStep.call(c, weight_type="ivm", blendheaders=False, save_results=True)
+
+    # assert_allclose(result1.data, result2.data)
+    # assert_allclose(result1.wht, result2.wht)
+    assert_allclose(result1.data[100:105, 100:105], 7.5, rtol=1e-2)
+    assert_allclose(result1.wht[100:105, 100:105], 20, rtol=1e-2)
+
+    result2 = ResampleStep.call(c, weight_type="exptime", blendheaders=False)
+
+    assert_allclose(result2.data[100:105, 100:105], 7.5, rtol=1e-2)
+    assert_allclose(result2.wht[100:105, 100:105], 20, rtol=1e-2)
+
+
+
 def test_sip_coeffs_do_not_propagate(nircam_rate):
     im = AssignWcsStep.call(nircam_rate, sip_degree=2)
 
diff --git a/jwst/resample/tests/test_utils.py b/jwst/resample/tests/test_utils.py
index c6c4955962..1951c88909 100644
--- a/jwst/resample/tests/test_utils.py
+++ b/jwst/resample/tests/test_utils.py
@@ -1,11 +1,15 @@
 """Test various utility functions"""
-import pytest
-
+from numpy.testing import assert_array_equal
 import numpy as np
+import pytest
 
-from jwst.datamodels import SlitModel
+from jwst.datamodels import SlitModel, ImageModel, dqflags
 from jwst.resample.resample_spec import find_dispersion_axis
-from jwst.resample.resample_utils import build_mask
+from jwst.resample.resample_utils import build_mask, build_driz_weight
+
+
+DO_NOT_USE = dqflags.pixel["DO_NOT_USE"]
+GOOD = dqflags.pixel["GOOD"]
 
 
 DQ = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
@@ -40,7 +44,32 @@ def test_build_mask(dq, bitvalues, expected):
         Expected mask array
     """
     result = build_mask(dq, bitvalues)
-    assert np.array_equal(result, expected)
+    assert_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("weight_type", ["ivm", "exptime"])
+def test_build_driz_weight(weight_type):
+    """Check that correct weight map is returned of different weight types"""
+    model = ImageModel((10, 10))
+    model.dq[0] = DO_NOT_USE
+    model.meta.exposure.exposure_time = 10.0
+    model.var_rnoise += 0.1
+
+    weight_map = build_driz_weight(model, weight_type=weight_type, good_bits="GOOD")
+    assert_array_equal(weight_map[0], 0)
+    assert_array_equal(weight_map[1:], 10.0)
+    assert weight_map.dtype == np.float32
+
+
+@pytest.mark.parametrize("weight_type", ["ivm", None])
+def test_build_driz_weight_zeros(weight_type):
+    """Check that zero or not finite weight maps get set to 1"""
+    model = ImageModel((10, 10))
+
+    with pytest.warns(RuntimeWarning):
+        weight_map = build_driz_weight(model, weight_type=weight_type)
+
+    assert_array_equal(weight_map, 1)
 
 
 def test_find_dispersion_axis():