diff --git a/config.yml b/config.yml
index 0b1f544..e8f4786 100644
--- a/config.yml
+++ b/config.yml
@@ -10,6 +10,9 @@ sql:
   mgrabase: "sql/mgrabase.sql"
   load_to_database: False
 
+datalake:
+  env: dev
+
 economic_controls: "data/Economic Team Region Controls.csv"
 
 synthesis_runs:
diff --git a/main.py b/main.py
index a9c7e28..f327a3e 100644
--- a/main.py
+++ b/main.py
@@ -13,7 +13,7 @@
 from python.build_controls import get_mgra_controls, get_region_controls
 from python.build_seed_data import get_seed_households, get_seed_persons
 from python.outputs import create_abm_outputs, organize_outputs
-from python.etl import run_etl
+from python.datalake_exporter import write_to_datalake
 from python.db import get_engine
 
 # Paths — anchored to this file so the pipeline can be run from any directory
@@ -148,14 +148,15 @@ def process_year(year: int, engine, config: dict, secrets: dict) -> None:
     )
 
     if config["sql"]["load_to_database"]:
-        run_id = run_etl(
-            year=year,
-            engine=engine,
-            output_database=secrets["sql"]["output_database"],
-            version=config["version"],
-            staging_schema=secrets["sql"]["schema"],
-            seed_data=config["seed_data"],
-            comments=config["comments"],
+        write_to_datalake(
+            output_path=f"output/{year}",
+            env=config["datalake"]["env"],
+            metadata={
+                "year": year,
+                "version": config["version"],
+                "seed_data": config["seed_data"],
+                "comments": config["comments"],
+            },
         )
 
 def main() -> None:
diff --git a/pyproject.toml b/pyproject.toml
index 551e753..f81af4b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ authors = [
 readme = "README.md"
 requires-python = ">=3.9,<3.13"
 dependencies = [
+    "azure-storage-blob>=12.29.0",
     "blosc2>=2.5.1,<3.0.0",
     "cvxpy[glpk]>=1.6.5,<2.0.0",
     "IPython>=8.0.0,<9.0.0",
diff --git a/python/datalake_exporter.py b/python/datalake_exporter.py
new file mode 100644
index 0000000..3914b6c
--- /dev/null
+++ b/python/datalake_exporter.py
@@ -0,0 +1,206 @@
+"""
+Exports PopulationSim CSV output files to Azure Data Lake Storage as Parquet files.
+
+Reads all CSVs from the specified output directory, converts them to Parquet,
+and uploads them to the datalake under popsim/<file>/<year>/<file>_<timestamp>.parquet.
+where <year> is the last segment of the output_path (e.g. '2022').
+The timestamp is derived from the modification time of synthetic_persons.csv.
+
+This layout lets Databricks Autoloader / Spark Declarative Pipelines point at
+popsim/<file>/ and ingest all runs for a given table across years.
+"""
+
+import datetime
+import glob
+import json
+import os
+import re
+import sys
+from io import BytesIO
+
+import pandas as pd
+from azure.core.exceptions import ResourceExistsError
+from azure.storage.blob import ContainerClient
+
+# -----------------------------------------------------------------------
+# HOW TO RUN
+# -----------------------------------------------------------------------
+#   Activate the sandag-population-sim venv
+#   Usage:          python datalake_exporter.py <output_path> <env>
+#   Example (dev):  python datalake_exporter.py C:\abm_runs\popsim_new\output\2022 dev
+#   Example (prod): python datalake_exporter.py C:\abm_runs\popsim_new\output\2022 prod
+#
+# Notes:
+#   - env must be 'dev' or 'prod'
+#   - output_path must contain synthetic_persons.csv
+#   - CSVs are converted to parquet and uploaded to: popsim/<file>/<year>/<file>_<timestamp>.parquet
+# -----------------------------------------------------------------------
+
+
+def connect_to_azure(env):
+    try:
+        if env == "dev":
+            sas_url = os.environ["AZURE_STORAGE_SAS_TOKEN_DS_DEV_SHARED"]
+        else:
+            sas_url = os.environ["AZURE_STORAGE_SAS_TOKEN_DS_PROD_SHARED"]
+        container = ContainerClient.from_container_url(sas_url)
+        container.get_account_information()
+        print("popsim exporter connected to Azure container")
+        return True, container
+    except KeyError as e:
+        print(
+            f"{e}: popsim exporter could not find SAS token in environment\n",
+            file=sys.stderr,
+        )
+        return False, None
+    except Exception as e:
+        print(
+            f"{e}: popsim exporter could not connect to Azure container\n",
+            file=sys.stderr,
+        )
+        return False, None
+
+
+def build_blob_path(*parts):
+    return "/".join(filter(None, parts))
+
+
+def get_lake_name(name):
+    # mgra15_based_input is the only output file with a year suffix baked into its
+    # name (e.g. "mgra15_based_input_2035"); strip it so every year lands under one
+    # lake path. 
+    return re.sub(r"^mgra15_based_input_\d{4}$", "mgra15_based_input", name)
+
+
+def export_csv_as_parquet(file, folder_name, ts_str, container, run_timestamp=None):
+    table = pd.read_csv(file, low_memory=False)
+    # year and run_timestamp are added as columns (not just encoded in the blob path) so
+    # Autoloader/Spark can partition and query across runs without parsing the file path.
+    if folder_name.isdigit():
+        table["year"] = int(folder_name)
+    if run_timestamp is not None:
+        table["run_timestamp"] = run_timestamp
+    name = os.path.splitext(os.path.basename(file))[0]
+    lake_name = get_lake_name(name)
+    lake_file_name = build_blob_path(
+        "popsim", lake_name, folder_name, lake_name + "_" + ts_str + ".parquet"
+    )
+    parquet_file = BytesIO()
+    table.to_parquet(parquet_file, engine="pyarrow")
+    parquet_file.seek(0)
+    t0 = datetime.datetime.now()
+    try:
+        container.upload_blob(name=lake_file_name, data=parquet_file)
+        elapsed = (datetime.datetime.now() - t0).total_seconds()
+        print(f"{lake_name} took {elapsed:.1f}s to write to Azure")
+        return True
+    except ResourceExistsError:
+        print(f"{lake_file_name} already exists in Azure, skipping", file=sys.stderr)
+        return False
+    except Exception as e:
+        print(f"Failed to upload {lake_name}: {e}", file=sys.stderr)
+        return False
+
+
+def write_to_datalake(output_path, env, metadata=None):
+    if not os.path.isdir(output_path):
+        print(
+            f"Output path does not exist or is not a directory: {output_path}",
+            file=sys.stderr,
+        )
+        return
+
+    cloud_bool, container = connect_to_azure(env)
+    if not cloud_bool:
+        return
+
+    folder_name = os.path.basename(output_path)
+
+    files = glob.glob(os.path.join(output_path, "*.csv"))
+    if not files:
+        print(f"No CSV files found in {output_path}", file=sys.stderr)
+        return
+
+    # synthetic_persons.csv is used as a reference file to derive the run timestamp.
+    # Its modification time represents when the PopulationSim run completed and is used to
+    # version the blob path, so each run's outputs are stored in a unique folder.
+    ref_file = os.path.join(output_path, "synthetic_persons.csv")
+    if not os.path.isfile(ref_file):
+        print(f"synthetic_persons.csv not found in {output_path}", file=sys.stderr)
+        return
+    created_ts = datetime.datetime.fromtimestamp(os.path.getmtime(ref_file))
+    ts_str = created_ts.strftime("%Y%m%d_%H%M%S")
+
+    succeeded = []
+    failed = []
+    for file in files:
+        ok = export_csv_as_parquet(
+            file, folder_name, ts_str, container, run_timestamp=created_ts
+        )
+        (succeeded if ok else failed).append(os.path.basename(file))
+
+    if metadata is not None:
+        meta_df = pd.DataFrame([metadata])
+        meta_blob = build_blob_path(
+            "popsim", "run_metadata", folder_name, "run_metadata_" + ts_str + ".parquet"
+        )
+        parquet_file = BytesIO()
+        meta_df.to_parquet(parquet_file, engine="pyarrow")
+        parquet_file.seek(0)
+        try:
+            container.upload_blob(name=meta_blob, data=parquet_file)
+            succeeded.append("run_metadata.parquet")
+            print("run_metadata.parquet written to Azure")
+        except ResourceExistsError:
+            print(f"{meta_blob} already exists in Azure, skipping", file=sys.stderr)
+            failed.append("run_metadata.parquet")
+        except Exception as e:
+            print(f"Failed to upload run_metadata: {e}", file=sys.stderr)
+            failed.append("run_metadata.parquet")
+
+    print(f"\nExport complete: {len(succeeded)} succeeded, {len(failed)} failed")
+    if failed:
+        print(f"Failed files: {', '.join(failed)}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    import yaml
+
+    if len(sys.argv) < 3:
+        print(
+            "Usage: python datalake_exporter_popsim.py <output_path> <env>",
+            file=sys.stderr,
+        )
+        print("  env must be 'dev' or 'prod'", file=sys.stderr)
+        sys.exit(1)
+    output_path = sys.argv[1]
+    env = sys.argv[2].lower()
+    if env not in {"dev", "prod"}:
+        print(
+            f"Error: env must be 'dev' or 'prod', got '{sys.argv[2]}'", file=sys.stderr
+        )
+        sys.exit(1)
+
+    # Build run_metadata from every top-level field in config.yml, plus the run year
+    # parsed from the output folder name. Nested/list fields (sql, synthesis_runs, years)
+    # are JSON-encoded so pandas/pyarrow can serialize them as flat string columns in the
+    # run_metadata parquet file. Skip metadata entirely if config.yml is missing/invalid.
+    config_path = os.path.join(os.path.dirname(__file__), "..", "config.yml")
+    metadata = None
+    try:
+        with open(config_path, "r") as f:
+            cfg = yaml.safe_load(f)
+        metadata = {
+            "year": int(year_str)
+            if (year_str := os.path.basename(output_path)).isdigit()
+            else year_str,
+        }
+        for key, value in cfg.items():
+            metadata[key] = json.dumps(value) if isinstance(value, (dict, list)) else value
+    except Exception as e:
+        print(
+            f"Could not load config.yml, run_metadata will be skipped: {e}",
+            file=sys.stderr,
+        )
+
+    write_to_datalake(output_path, env, metadata=metadata)
diff --git a/uv.lock b/uv.lock
index 0228d2f..eba20bc 100644
--- a/uv.lock
+++ b/uv.lock
@@ -5,7 +5,8 @@ resolution-markers = [
     "python_full_version >= '3.12'",
     "python_full_version == '3.11.*'",
     "python_full_version == '3.10.*'",
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 
 [[package]]
@@ -13,7 +14,8 @@ name = "absl-py"
 version = "2.3.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
 wheels = [
@@ -39,7 +41,8 @@ name = "altair"
 version = "5.5.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 dependencies = [
     { name = "jinja2", marker = "python_full_version < '3.10'" },
@@ -92,12 +95,65 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
 ]
 
+[[package]]
+name = "azure-core"
+version = "1.39.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
+]
+dependencies = [
+    { name = "requests", marker = "python_full_version < '3.10'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/34/83/bbde3faa84ddcb8eb0eca4b3ffb3221252281db4ce351300fe248c5c70b1/azure_core-1.39.0.tar.gz", hash = "sha256:8a90a562998dd44ce84597590fff6249701b98c0e8797c95fcdd695b54c35d74", size = 367531, upload-time = "2026-03-19T01:31:29.461Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/d6/8ebcd05b01a580f086ac9a97fb9fac65c09a4b012161cc97c21a336e880b/azure_core-1.39.0-py3-none-any.whl", hash = "sha256:4ac7b70fab5438c3f68770649a78daf97833caa83827f91df9c14e0e0ea7d34f", size = 218318, upload-time = "2026-03-19T01:31:31.25Z" },
+]
+
+[[package]]
+name = "azure-core"
+version = "1.41.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+]
+dependencies = [
+    { name = "requests", marker = "python_full_version >= '3.10'" },
+    { name = "typing-extensions", marker = "python_full_version >= '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a6/f3/b416179e408990df5db0d516283022dde0f5d0111d98c1a848e41853e81c/azure_core-1.41.0.tar.gz", hash = "sha256:f46ff5dfcd230f25cf1c19e8a34b8dc08a337b2503e268bb600a16c00db8ad5a", size = 381042, upload-time = "2026-05-07T23:30:54.302Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5b/db/325c6d7312d2200251c52323878281045aaffcb5586612296484e4280eaa/azure_core-1.41.0-py3-none-any.whl", hash = "sha256:522b4011e8180b1a3dcd2024396a4e7fe9ac37fb8597db47163d230b5efe892d", size = 220920, upload-time = "2026-05-07T23:30:56.357Z" },
+]
+
+[[package]]
+name = "azure-storage-blob"
+version = "12.29.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "azure-core", version = "1.39.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "azure-core", version = "1.41.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "cryptography", version = "47.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version <= '3.9'" },
+    { name = "cryptography", version = "48.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version > '3.9'" },
+    { name = "isodate" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/59/25/fdcf1e381922dbab8ba23d6fd78d397fe6cbac6b480310218834b7bc91fe/azure_storage_blob-12.29.0.tar.gz", hash = "sha256:2824ddd7ebc9056034ebc76b17971a38e9aa5835abb0d565b9700493f2a6c657", size = 611359, upload-time = "2026-05-15T03:34:59.865Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/2c/6ddee6a3e42d0236ba9259e4df7fa97fdc415ff0802b736c634baaf4b285/azure_storage_blob-12.29.0-py3-none-any.whl", hash = "sha256:ccf8a1bcd5e49df83ab85aab793b579e5ba2eeea2ad8900b2f62ca3a37dc391f", size = 434823, upload-time = "2026-05-15T03:35:01.837Z" },
+]
+
 [[package]]
 name = "black"
 version = "25.11.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 dependencies = [
     { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
@@ -183,7 +239,8 @@ name = "blosc2"
 version = "2.5.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 dependencies = [
     { name = "msgpack", marker = "python_full_version < '3.10'" },
@@ -348,7 +405,8 @@ name = "cfgv"
 version = "3.4.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/11/74/539e56497d9bd1d484fd863dd69cbbfa653cd2aa27abfe35653494d85e94/cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560", size = 7114, upload-time = "2023-08-12T20:38:17.776Z" }
 wheels = [
@@ -467,7 +525,8 @@ name = "click"
 version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 dependencies = [
     { name = "colorama", marker = "python_full_version < '3.10' and sys_platform == 'win32'" },
@@ -508,7 +567,8 @@ name = "contourpy"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 dependencies = [
     { name = "numpy", marker = "python_full_version < '3.10'" },
@@ -660,7 +720,8 @@ name = "coverage"
 version = "7.10.7"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
+    "python_full_version <= '3.9'",
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/51/26/d22c300112504f5f9a9fd2297ce33c35f3d353e4aeb987c8419453b2a7c2/coverage-7.10.7.tar.gz", hash = "sha256:f4ab143ab113be368a3e9b795f9cd7906c5ef407d6173fe9675a902e1fffc239", size = 827704, upload-time = "2025-09-21T20:03:56.815Z" }
 wheels = [
@@ -785,6 +846,107 @@ toml = [
     { name = "tomli", marker = "python_full_version >= '3.10' and python_full_version <= '3.11'" },
 ]
 
+[[package]]
+name = "cryptography"
+version = "47.0.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version <= '3.9'",
+]
+dependencies = [
+    { name = "cffi", marker = "python_full_version <= '3.9' and platform_python_implementation != 'PyPy'" },
+    { name = "typing-extensions", marker = "python_full_version <= '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ef/b2/7ffa7fe8207a8c42147ffe70c3e360b228160c1d85dc3faff16aaa3244c0/cryptography-47.0.0.tar.gz", hash = "sha256:9f8e55fe4e63613a5e1cc5819030f27b97742d720203a087802ce4ce9ceb52bb", size = 830863, upload-time = "2026-04-24T19:54:57.056Z" }
+wheels = [
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-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
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 version = "0.36.2"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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+    "python_full_version > '3.9' and python_full_version < '3.10'",
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 version = "0.27.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.10'",
+    "python_full_version > '3.9' and python_full_version < '3.10'",
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@@ -3098,6 +3288,7 @@ name = "sandag-population-sim"
 version = "0.10.0"
 source = { virtual = "." }
 dependencies = [
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     { name = "cvxpy", extra = ["glpk"] },
@@ -3139,6 +3330,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
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 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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