Data platforms, AI systems, and the architecture that keeps them running.

50% faster core processingrole-level before/after wall-clock on the rebuilt Spark/Airflow path — not a lab sample · Data Engineer role, 2020–2024

40% lower infrastructure costrole-level before/after infrastructure run-rate on the Kubernetes migration — not a lab sample · Data Engineer role, 2020–2024

≥ 6 s per idle shard, every batch, on the alternative connectorconnector source plus staging Spark UI stage timings; floor = empty-poll count × poll timeout, and the connector requires that product to exceed 5 s, so 6 s is the lowest it accepts · Aug 2026, staging

9 of 9 failure runs with every event layer exactthree controller-broker kills, three TaskManager kills and three 150 s stalls under load; Kafka, ClickHouse, Iceberg and the dead-letter topic reconciled against the generator's ledger by sequence number · Oct 2026

59.8% of on-time events left in the window counts after one broker kill118,229 of 197,749 on-time events counted in their 10 s windows in the worst of three broker-kill runs; the rest went to the late side output because catch-up reordered partitions past the 5 s watermark bound · Oct 2026

0 breaking schema changes passed as safe, in any mode51 hand-labelled JSON Schema changes, each scored under BACKWARD, FORWARD and FULL; every label cross-checked by an oracle that ran 8,092 generated probe documents through a real JSON Schema validator · measured Oct 2026

2 of 51 changes flagged as breaking that are safe, per directionthe same 51 cases; a widened and a narrowed regular expression, an added anyOf branch and an added multipleOf are flagged in both directions by design, and each truly breaks only one · measured Oct 2026

88 ms one-month predicate over 1.5M Iceberg rowsmedian of 7 server-side runs from system.runtime.queries, after 2 warm-ups, on a month-partitioned Iceberg table · measured 2026 · 2024 stack rebuilt

909,968 rows recovered by snapshot rollbackan over-broad DELETE (a WHERE reaching years further back than intended), then ALTER TABLE EXECUTE rollback_to_snapshot, counted against the pre-incident total · measured 2026 · 2024 stack rebuilt

60% faster analytics deliveryrole-level before/after delivery time for recurring analytics requests — not a lab sample · Senior Data Engineer role, 2024–present

75% less analyst manual effortrole-level before/after manual effort per recurring analyst request — not a lab sample · Senior Data Engineer role, 2024–present

p = 0.39 for a 4.8-point judge drop the gate declined to failexact McNemar on 83 paired cases — 8 worse, 4 better, 71 unchanged — qwen3:1.7b judged by qwen3:8b on Ollama, temperature 0 · measured Oct 2026

1,469 → 4,422 ms p95 latency, the regression that was realsystem latency over the same 83 cases, sequential, one Apple M5; mean answer length went from 19 to 77 tokens · measured Oct 2026

48/54 answers matched an answer key computed by SQLone live run of a 54-question golden set against qwen3:8b under Ollama, temperature 0, thinking off, over a seeded synthetic warehouse; 95% CI 78–95% · Oct 2026, one recorded run

3 of 37 answers published without review were wrongsame run; a finding is published only after its schema, policy and grounding checks pass with no proposal attached; 95% Wilson interval on 3/37 is 3–21% · Oct 2026, one recorded run

0.363 vs 0.941 future AUC-PR, naive join against point-in-time joinsame rows, labels, model and hyperparameters; only the training join differs. Scored on the two weeks after training with point-in-time features and true labels, on seeded synthetic payments (874,446 transactions, 0.80% fraud) · measured 2026-10 · seeded synthetic data

82.5% of fraud cost saved on two unseen weeksa $5 review per flag plus the amount of every missed fraud, against approving everything; threshold chosen on the validation window, true labels. A hand-written rule saved 20.7% · measured 2026-10 · seeded synthetic data

95.8% of matched lab-result values read correctlyfield-level scorer on 97 labelled pages of real clinic captures; development set, not clinical validation · Aug–Sep 2026

8 OCR engines scored through one fixed scorersame extraction pipeline and field-level scorer; 11 + 26 labelled pages for classic engines, one 17-page packet for vision-language models · Aug 2026

3.9 of 10 rows returned by post-filtered HNSW at 10% selectivitypgvector 0.8.7 HNSW at ef_search 40 with iterative scan off, 1,000 queries over 381,559 vectors, a filter keeping 10% of rows; recall@10 0.391 against exact kNN over the rows that pass · measured 2026

0.745 recall@10 of OpenSearch's efficient filter at 10% selectivityfilter inside the knn clause, Faiss HNSW at ef_search 40, 1,000 queries over 381,559 vectors, against exact kNN over the rows that pass · measured 2026

0 errors while a worker joined and 7 shard groups moved under load8 client threads for 600 s, 80% one-tenant reads and 20% single-row inserts against the Citus coordinator; every acknowledged write counted back afterwards · measured Oct 2026

4.2× faster join co-located than repartitionedthe same join over tenants 1–400, both returning 617,895 rows; p50 of 7 runs, 1,251.7 ms co-located on (tenant_id, user_id) vs 5,206.0 ms against a copy distributed by user_id · measured Oct 2026

8 of 8 failover scenarios passedmy own test plan, executed on Docker with 8 CPU / 12.5 GB · 2026

383 µs replay lag measured on one hosta single pg_stat_replication reading with primary and hot standby on the same machine — loopback, no network, so it is a floor for the configuration rather than a latency anyone should expect across hosts

10–20 s observed promotion window after SIGKILLSIGKILL of the Patroni leader, then patronictl polled every 10 s until the survivor showed as Leader — so 10 s is the measurement resolution, not the precision of the result · 2026

~5 s replica rebuilt from backuppatronictl reinit; the pgBackRest restore itself logged 5,020 ms, then Patroni reported the replica created · 2026

6 of 6 bad releases aborted by canary analysisa release whose Envoy sidecar returns 500 on 30% of requests, under 20 req/s from k6; three runs with keep-alive clients, three with a new connection per request; Prometheus analysis every 15 s after a 30 s initial delay · measured Oct 2026 · kind, three nodes

0 of 18,003 requests failed across three good canary releasesk6 at 20 req/s for five minutes per run, GET /healthz and GET /review-queue with a 10 s timeout; canary 20% → 50% with 60 s pauses, background analysis and an evidence step before 100% · measured Oct 2026 · kind, three nodes

90+ SQL migrationsversioned migration files in the repository