diff --git a/cpp/src/io/parquet/page_enc.cu b/cpp/src/io/parquet/page_enc.cu
index d066b454840..0af561be8da 100644
--- a/cpp/src/io/parquet/page_enc.cu
+++ b/cpp/src/io/parquet/page_enc.cu
@@ -229,6 +229,16 @@ Encoding __device__ determine_encoding(PageType page_type,
   }
 }
 
+// operator to use with warp_reduce. stolen from cub::Sum
+struct BitwiseOr {
+  /// Binary OR operator, returns <tt>a | b</tt>
+  template <typename T>
+  __host__ __device__ __forceinline__ T operator()(T const& a, T const& b) const
+  {
+    return a | b;
+  }
+};
+
 }  // anonymous namespace
 
 // blockDim {512,1,1}
@@ -1445,6 +1455,7 @@ __global__ void __launch_bounds__(decide_compression_block_size)
 
   uint32_t uncompressed_data_size = 0;
   uint32_t compressed_data_size   = 0;
+  uint32_t encodings              = 0;
   auto const num_pages            = ck_g[warp_id].num_pages;
   for (auto page_id = lane_id; page_id < num_pages; page_id += cudf::detail::warp_size) {
     auto const& curr_page     = ck_g[warp_id].pages[page_id];
@@ -1457,10 +1468,14 @@ __global__ void __launch_bounds__(decide_compression_block_size)
         atomicOr(&compression_error[warp_id], 1);
       }
     }
+    // collect encoding info for the chunk metadata
+    encodings |= encoding_to_mask(curr_page.encoding);
   }
   uncompressed_data_size = warp_reduce(temp_storage[warp_id][0]).Sum(uncompressed_data_size);
   compressed_data_size   = warp_reduce(temp_storage[warp_id][1]).Sum(compressed_data_size);
   __syncwarp();
+  encodings = warp_reduce(temp_storage[warp_id][0]).Reduce(encodings, BitwiseOr{});
+  __syncwarp();
 
   if (lane_id == 0) {
     auto const write_compressed = compressed_data_size != 0 and compression_error[warp_id] == 0 and
@@ -1469,6 +1484,12 @@ __global__ void __launch_bounds__(decide_compression_block_size)
     chunks[chunk_id].bfr_size      = uncompressed_data_size;
     chunks[chunk_id].compressed_size =
       write_compressed ? compressed_data_size : uncompressed_data_size;
+
+    // if there is repetition or definition level data add RLE encoding
+    auto const rle_bits =
+      ck_g[warp_id].col_desc->num_def_level_bits() + ck_g[warp_id].col_desc->num_rep_level_bits();
+    if (rle_bits > 0) { encodings |= encoding_to_mask(Encoding::RLE); }
+    chunks[chunk_id].encodings = encodings;
   }
 }
 
diff --git a/cpp/src/io/parquet/parquet_common.hpp b/cpp/src/io/parquet/parquet_common.hpp
index ab6290c4ed6..5f8f1617cb9 100644
--- a/cpp/src/io/parquet/parquet_common.hpp
+++ b/cpp/src/io/parquet/parquet_common.hpp
@@ -92,6 +92,7 @@ enum class Encoding : uint8_t {
   DELTA_BYTE_ARRAY        = 7,
   RLE_DICTIONARY          = 8,
   BYTE_STREAM_SPLIT       = 9,
+  NUM_ENCODINGS           = 10,
 };
 
 /**
diff --git a/cpp/src/io/parquet/parquet_gpu.hpp b/cpp/src/io/parquet/parquet_gpu.hpp
index 0a8640aef26..e82b6abc13d 100644
--- a/cpp/src/io/parquet/parquet_gpu.hpp
+++ b/cpp/src/io/parquet/parquet_gpu.hpp
@@ -345,8 +345,8 @@ struct parquet_column_device_view : stats_column_desc {
   ConvertedType converted_type;  //!< logical data type
   uint8_t level_bits;  //!< bits to encode max definition (lower nibble) & repetition (upper nibble)
                        //!< levels
-  constexpr uint8_t num_def_level_bits() { return level_bits & 0xf; }
-  constexpr uint8_t num_rep_level_bits() { return level_bits >> 4; }
+  constexpr uint8_t num_def_level_bits() const { return level_bits & 0xf; }
+  constexpr uint8_t num_rep_level_bits() const { return level_bits >> 4; }
   size_type const* const*
     nesting_offsets;  //!< If column is a nested type, contains offset array of each nesting level
 
@@ -384,6 +384,12 @@ constexpr size_t kDictScratchSize    = (1 << kDictHashBits) * sizeof(uint32_t);
 struct EncPage;
 struct slot_type;
 
+// convert Encoding to a mask value
+constexpr uint32_t encoding_to_mask(Encoding encoding)
+{
+  return 1 << static_cast<uint32_t>(encoding);
+}
+
 /**
  * @brief Struct describing an encoder column chunk
  */
@@ -420,6 +426,7 @@ struct EncColumnChunk {
   bool use_dictionary;    //!< True if the chunk uses dictionary encoding
   uint8_t* column_index_blob;  //!< Binary blob containing encoded column index for this chunk
   uint32_t column_index_size;  //!< Size of column index blob
+  uint32_t encodings;          //!< Mask representing the set of encodings used for this chunk
 };
 
 /**
@@ -748,6 +755,8 @@ void EncodePages(device_span<EncPage> pages,
 /**
  * @brief Launches kernel to make the compressed vs uncompressed chunk-level decision
  *
+ * Also calculates the set of page encodings used for each chunk.
+ *
  * @param[in,out] chunks Column chunks (updated with actual compressed/uncompressed sizes)
  * @param[in] stream CUDA stream to use
  */
diff --git a/cpp/src/io/parquet/writer_impl.cu b/cpp/src/io/parquet/writer_impl.cu
index c5fc852d20b..d2976a3f5d9 100644
--- a/cpp/src/io/parquet/writer_impl.cu
+++ b/cpp/src/io/parquet/writer_impl.cu
@@ -193,6 +193,20 @@ parquet::Compression to_parquet_compression(compression_type compression)
   }
 }
 
+/**
+ * @brief Convert a mask of encodings to a vector.
+ *
+ * @param encodings Vector of `Encoding`s to populate
+ * @param enc_mask Mask of encodings used
+ */
+void update_chunk_encodings(std::vector<Encoding>& encodings, uint32_t enc_mask)
+{
+  for (uint8_t enc = 0; enc < static_cast<uint8_t>(Encoding::NUM_ENCODINGS); enc++) {
+    auto const enc_enum = static_cast<Encoding>(enc);
+    if ((enc_mask & gpu::encoding_to_mask(enc_enum)) != 0) { encodings.push_back(enc_enum); }
+  }
+}
+
 /**
  * @brief Compute size (in bytes) of the data stored in the given column.
  *
@@ -1671,6 +1685,7 @@ auto convert_table_to_parquet_data(table_input_metadata& table_meta,
         ck.start_row         = start_row;
         ck.num_rows          = (uint32_t)row_group.num_rows;
         ck.first_fragment    = c * num_fragments + f;
+        ck.encodings         = 0;
         auto chunk_fragments = row_group_fragments[c].subspan(f, fragments_in_chunk);
         // In fragment struct, add a pointer to the chunk it belongs to
         // In each fragment in chunk_fragments, update the chunk pointer here.
@@ -1687,7 +1702,6 @@ auto convert_table_to_parquet_data(table_input_metadata& table_meta,
           });
         auto& column_chunk_meta          = row_group.columns[c].meta_data;
         column_chunk_meta.type           = parquet_columns[c].physical_type();
-        column_chunk_meta.encodings      = {Encoding::PLAIN, Encoding::RLE};
         column_chunk_meta.path_in_schema = parquet_columns[c].get_path_in_schema();
         column_chunk_meta.codec          = UNCOMPRESSED;
         column_chunk_meta.num_values     = ck.num_values;
@@ -1703,17 +1717,6 @@ auto convert_table_to_parquet_data(table_input_metadata& table_meta,
   row_group_fragments.host_to_device_async(stream);
   [[maybe_unused]] auto dict_info_owner = build_chunk_dictionaries(
     chunks, col_desc, row_group_fragments, compression, dict_policy, max_dictionary_size, stream);
-  for (size_t p = 0; p < partitions.size(); p++) {
-    for (int rg = 0; rg < num_rg_in_part[p]; rg++) {
-      size_t global_rg = global_rowgroup_base[p] + rg;
-      for (int col = 0; col < num_columns; col++) {
-        if (chunks.host_view()[rg][col].use_dictionary) {
-          agg_meta->file(p).row_groups[global_rg].columns[col].meta_data.encodings.push_back(
-            Encoding::PLAIN_DICTIONARY);
-        }
-      }
-    }
-  }
 
   // The code preceding this used a uniform fragment size for all columns. Now recompute
   // fragments with a (potentially) varying number of fragments per column.
@@ -1949,6 +1952,8 @@ auto convert_table_to_parquet_data(table_input_metadata& table_meta,
         }
         max_write_size = std::max(max_write_size, ck.compressed_size);
 
+        update_chunk_encodings(column_chunk_meta.encodings, ck.encodings);
+
         if (ck.ck_stat_size != 0) {
           std::vector<uint8_t> const stats_blob = cudf::detail::make_std_vector_sync(
             device_span<uint8_t const>(dev_bfr, ck.ck_stat_size), stream);
diff --git a/cpp/tests/io/parquet_test.cpp b/cpp/tests/io/parquet_test.cpp
index 8c7d598d33f..b210452f619 100644
--- a/cpp/tests/io/parquet_test.cpp
+++ b/cpp/tests/io/parquet_test.cpp
@@ -6599,4 +6599,74 @@ TEST_F(ParquetWriterTest, TimestampMicrosINT96NoOverflow)
   CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
 }
 
+TEST_P(ParquetV2Test, CheckEncodings)
+{
+  using cudf::io::parquet::Encoding;
+  constexpr auto num_rows = 100'000;
+  auto const is_v2        = GetParam();
+
+  auto const validity = cudf::test::iterators::no_nulls();
+  // data should be PLAIN for v1, RLE for V2
+  auto col0_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) -> bool { return i % 2 == 0; });
+  // data should be PLAIN for both
+  auto col1_data = random_values<int32_t>(num_rows);
+  // data should be PLAIN_DICTIONARY for v1, PLAIN and RLE_DICTIONARY for v2
+  auto col2_data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 1; });
+
+  cudf::test::fixed_width_column_wrapper<bool> col0{col0_data, col0_data + num_rows, validity};
+  column_wrapper<int32_t> col1{col1_data.begin(), col1_data.end(), validity};
+  column_wrapper<int32_t> col2{col2_data, col2_data + num_rows, validity};
+
+  auto expected = table_view{{col0, col1, col2}};
+
+  auto const filename = is_v2 ? "CheckEncodingsV2.parquet" : "CheckEncodingsV1.parquet";
+  auto filepath       = temp_env->get_temp_filepath(filename);
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .max_page_size_rows(num_rows)
+      .write_v2_headers(is_v2);
+  cudf::io::write_parquet(out_opts);
+
+  // make sure the expected encodings are present
+  auto contains = [](auto const& vec, auto const& enc) {
+    return std::find(vec.begin(), vec.end(), enc) != vec.end();
+  };
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  auto const& chunk0_enc = fmd.row_groups[0].columns[0].meta_data.encodings;
+  auto const& chunk1_enc = fmd.row_groups[0].columns[1].meta_data.encodings;
+  auto const& chunk2_enc = fmd.row_groups[0].columns[2].meta_data.encodings;
+  if (is_v2) {
+    // col0 should have RLE for rep/def and data
+    EXPECT_TRUE(chunk0_enc.size() == 1);
+    EXPECT_TRUE(contains(chunk0_enc, Encoding::RLE));
+    // col1 should have RLE for rep/def and PLAIN for data
+    EXPECT_TRUE(chunk1_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::PLAIN));
+    // col2 should have RLE for rep/def, PLAIN for dict, and RLE_DICTIONARY for data
+    EXPECT_TRUE(chunk2_enc.size() == 3);
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::PLAIN));
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::RLE_DICTIONARY));
+  } else {
+    // col0 should have RLE for rep/def and PLAIN for data
+    EXPECT_TRUE(chunk0_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk0_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk0_enc, Encoding::PLAIN));
+    // col1 should have RLE for rep/def and PLAIN for data
+    EXPECT_TRUE(chunk1_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::PLAIN));
+    // col2 should have RLE for rep/def and PLAIN_DICTIONARY for data and dict
+    EXPECT_TRUE(chunk2_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::PLAIN_DICTIONARY));
+  }
+}
+
 CUDF_TEST_PROGRAM_MAIN()