From 46094645b16e23e2229ca3bd1ad15940cd86b7c8 Mon Sep 17 00:00:00 2001
From: ahuston-0 <aliceghuston@gmail.com>
Date: Thu, 30 Apr 2026 14:09:34 -0400
Subject: [PATCH] format, flake update

---
 flake.lock |  6 +++---
 resume.tex | 26 +++++++++++++-------------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/flake.lock b/flake.lock
index f194334..3f06416 100644
--- a/flake.lock
+++ b/flake.lock
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1774386573,
-        "narHash": "sha256-4hAV26quOxdC6iyG7kYaZcM3VOskcPUrdCQd/nx8obc=",
+        "lastModified": 1777268161,
+        "narHash": "sha256-bxrdOn8SCOv8tN4JbTF/TXq7kjo9ag4M+C8yzzIRYbE=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "46db2e09e1d3f113a13c0d7b81e2f221c63b8ce9",
+        "rev": "1c3fe55ad329cbcb28471bb30f05c9827f724c76",
         "type": "github"
       },
       "original": {
diff --git a/resume.tex b/resume.tex
index e8eb7ff..9f616f9 100644
--- a/resume.tex
+++ b/resume.tex
@@ -155,25 +155,25 @@
 {JPMorgan Chase}{Jersey City, NJ}
 \resumeItemListStart
 \resumeItem{Designed and deployed configurable data ingestion framework
-using Iceberg CTAS and time-travel for zero-outage updates,
-orchestrating 200+ refinement pipelines with automated data
+  using Iceberg CTAS and time-travel for zero-outage updates,
+  orchestrating 200+ refinement pipelines with automated data
 reconciliation across four zones (OLTP, raw, trusted, refined)}
 \resumeItem{Implemented PyArrow-based validation and dual-engine
-architecture supporting on-prem (Starburst) and off-prem (Databricks)
+  architecture supporting on-prem (Starburst) and off-prem (Databricks)
 reporting for 50+ downstream teams}
 \resumeItem{Architected and implemented Apache Airflow orchestration
-supporting 1,000+ tasks per DAG with templated configuration-driven
-design, tiered pooling to prevent resource exhaustion, and automated
+  supporting 1,000+ tasks per DAG with templated configuration-driven
+  design, tiered pooling to prevent resource exhaustion, and automated
 partition registration in Trino for large Hive tables}
 \resumeItem{Led weekly office hours to help onboard new datasets and
-trained 10 developers to operate and extend the framework across
+  trained 10 developers to operate and extend the framework across
 multiple applications, reducing MTTR for incidents}
 \resumeItem{Led Kubernetes resource optimization across 30+ services in
-three applications, implementing best-effort QoS in dev and test
-environments while tuning production resources, achieving \$50k
+  three applications, implementing best-effort QoS in dev and test
+  environments while tuning production resources, achieving \$50k
 annual cost savings in reservations and usage}
 \resumeItem{Created reusable Helm charts and a shared service layer
-that enabled 4 platform teams to deploy and configure UI services
+  that enabled 4 platform teams to deploy and configure UI services
 more consistently}
 \resumeItemListEnd
 
@@ -185,10 +185,10 @@ more consistently}
 {JPMorgan Chase}{Jersey City, NJ}
 \resumeItemListStart
 \resumeItem{Owned production support for 30 applications across
-multiple teams, including deployment approvals, incident response,
+  multiple teams, including deployment approvals, incident response,
 root cause analysis, and post-mortems}
 \resumeItem{Served as primary support engineer for a Hadoop-based data
-lake platform spanning Tableau, Kubernetes, Cloud Foundry, Dremio,
+  lake platform spanning Tableau, Kubernetes, Cloud Foundry, Dremio,
 and S3-compatible object storage}
 \resumeItem{Served as the team expert on Linux, networking, and
 Hadoop infrastructure supporting business-critical applications}
@@ -199,8 +199,8 @@ applications, improving alert coverage and observability consistency}
 \resumeItem{Automated disaster recovery procedures for a subset of
 production applications, reducing manual failover steps}
 \resumeItem{Automated historical data reload workflows using backup
-cluster for reprocessing and merge back to primary Hive datasets,
-reducing 72 hours of manual effort to zero and enabling on-demand
+  cluster for reprocessing and merge back to primary Hive datasets,
+  reducing 72 hours of manual effort to zero and enabling on-demand
 backfill capabilities}
 \resumeItemListEnd