diff --git a/setup.cfg b/setup.cfg
index 960d575..1687d78 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -58,6 +58,7 @@ pyaro.timeseries =
     harp = pyaro_readers.harpreader:AeronetHARPEngine
     nilupmfabsorption = pyaro_readers.nilupmfabsorptionreader:NILUPMFAbsorptionTimeseriesEngine
     nilupmfebas = pyaro_readers.nilupmfebas:EbasPmfTimeseriesEngine
+    parquet = pyaro_readers.parquet:ParquetTimeseriesEngine
 
 
 
diff --git a/src/pyaro_readers/parquet/__init__.py b/src/pyaro_readers/parquet/__init__.py
new file mode 100644
index 0000000..13ed99f
--- /dev/null
+++ b/src/pyaro_readers/parquet/__init__.py
@@ -0,0 +1,147 @@
+from pyaro.timeseries.AutoFilterReaderEngine import AutoFilterReader, AutoFilterEngine
+from pyaro.timeseries import Reader, Data, Station
+import polars
+import numpy as np
+
+
+class ParquetReaderException(Exception):
+    pass
+
+
+class ParquetData(Data):
+    def __init__(self, dataset: polars.DataFrame, variable: str):
+        self._variable = variable
+        self._dataset = dataset
+
+    def __len__(self) -> int:
+        return len(self._dataset)
+
+    def slice(self, index):
+        return ParquetData(self._dataset[index], self._variable)
+
+    @property
+    def altitudes(self):
+        return self._dataset["altitude"].to_numpy()
+
+    @property
+    def start_times(self):
+        return self._dataset["start_time"].to_numpy()
+
+    @property
+    def end_times(self):
+        return self._dataset["end_time"].to_numpy()
+
+    @property
+    def flags(self):
+        return self._dataset["flag"].to_numpy()
+
+    def keys(self):
+        return set(self._dataset.columns) - set(["variable", "units"])
+
+    @property
+    def latitudes(self):
+        return self._dataset["latitude"].to_numpy()
+
+    @property
+    def longitudes(self):
+        return self._dataset["longitude"].to_numpy()
+
+    @property
+    def standard_deviations(self):
+        return self._dataset["standard_deviation"].to_numpy()
+
+    @property
+    def stations(self):
+        return self._dataset["station"].to_numpy()
+
+    @property
+    def values(self):
+        return self._dataset["value"].to_numpy()
+
+    @property
+    def units(self):
+        units = self._dataset["units"].unique()
+        if len(units) > 1:
+            raise ParquetReaderException(
+                f"This dataset contains more than one unit: {units}"
+            )
+        return units[0]
+
+
+class ParquetTimeseriesReader(AutoFilterReader):
+    MANDATORY_COLUMNS = {
+        "variable",
+        "units",
+        "value",
+        "station",
+        "longitude",
+        "latitude",
+        "start_time",
+        "end_time",
+    }
+    OPTIONAL_COLUMNS = {
+        "country": "",
+        "flag": 0,
+        "altitude": np.nan,
+        "standard_deviation": np.nan,
+    }
+
+    def __init__(self, filename: str, filters):
+        self._set_filters(filters)
+        dataset = polars.read_parquet(filename)
+
+        ds_cols = dataset.columns
+        missing_mandatory = self.MANDATORY_COLUMNS - set(ds_cols)
+        if len(missing_mandatory):
+            raise ParquetReaderException(
+                f"Expected the mandatory columns missing: {missing_mandatory}"
+            )
+
+        missing_optional = set(self.OPTIONAL_COLUMNS.keys()) - set(ds_cols)
+        for missing in missing_optional:
+            dataset = dataset.with_columns(
+                polars.lit(self.OPTIONAL_COLUMNS[missing]).alias(missing)
+            )
+
+        self.dataset = dataset
+
+    def _unfiltered_data(self, varname: str) -> ParquetData:
+        return ParquetData(
+            self.dataset.filter(polars.col("variable").eq(varname)), varname
+        )
+
+    def _unfiltered_stations(self) -> dict[str, Station]:
+        ds = self.dataset.group_by("station").first()
+
+        stations = dict()
+        for row in ds.rows(named=True):
+            stations[row["station"]] = Station(
+                {
+                    "station": row["station"],
+                    "longitude": row["longitude"],
+                    "latitude": row["latitude"],
+                    "altitude": row["altitude"],
+                    "country": row["country"],
+                    "url": "",
+                    "long_name": row["station"],
+                }
+            )
+        return stations
+
+    def _unfiltered_variables(self) -> list[str]:
+        return list(self.dataset["variable"].unique())
+
+    def close(self):
+        pass
+
+
+class ParquetTimeseriesEngine(AutoFilterEngine):
+    def description(self) -> str:
+        return """Parquet reader
+        """
+
+    def url(self) -> str:
+        return "https://github.com/metno/pyaro-readers"
+
+    def reader_class(self) -> AutoFilterReader:
+        return ParquetTimeseriesReader
diff --git a/tests/test_parquetreader.py b/tests/test_parquetreader.py
new file mode 100644
index 0000000..0d5dcfd
--- /dev/null
+++ b/tests/test_parquetreader.py
@@ -0,0 +1,51 @@
+from io import BytesIO
+
+import numpy as np
+import polars
+import pandas
+
+from pyaro_readers.parquet import ParquetTimeseriesReader
+
+
+def test_reading():
+    N = 1000
+
+    times = pandas.date_range("2025-02-28 00:00", freq="1h", periods=N + 1)
+
+    ds_tmp = polars.DataFrame(
+        {
+            "variable": "vespene",
+            "units": "kg/m^3",
+            "value": np.random.random(N),
+            "station": "base",
+            "longitude": 10,
+            "latitude": 59,
+            "start_time": times[:-1],
+            "end_time": times[1:],
+        }
+    )
+    tmpfile = BytesIO()
+    ds_tmp.write_parquet(tmpfile)
+    tmpfile.seek(0)
+
+    ds = ParquetTimeseriesReader(tmpfile, filters=[])
+
+    stations = ds.stations()
+    assert len(stations) == 1
+    station = stations["base"]
+    assert station.longitude == 10
+    assert station.latitude == 59
+    assert station.long_name == "base"
+
+    assert np.unique(ds.variables()) == ["vespene"]
+
+    data = ds.data("vespene")
+
+    assert np.all(data.start_times == times[:-1])
+    assert np.all(data.end_times == times[1:])
+    assert np.all(ds_tmp["value"].to_numpy() == data.values)
+
+    data_slice = data[:500]
+    assert len(data_slice) == 500
+    assert np.all(ds_tmp["value"][:500].to_numpy() == data_slice.values)
+    assert data.units == "kg/m^3"