Data Engineer at SimanPro · West Haven, Connecticut
Pipelines that carry data, and models that read it.
I build the batch and streaming infrastructure that moves data at scale —
Spark, Airflow, dbt, Kafka, Snowflake — and co-author applied machine
learning research across green computing, industrial prognostics, financial
risk and medical imaging.
I move data for a living, and I co-author machine learning research on the side.
They look separate. They are not — most of what goes wrong in a published model
is a data problem, and most of what I fix at work is a data problem.
⏱
Ingest it
Batch and streaming pipelines over structured and unstructured sources.
Spark, Airflow, dbt, Kafka. Late-arriving events, replay after an outage,
and producers that change schema without telling anyone.
◧
Model it
Warehouse and lakehouse design — staging, conformed dimensions, slowly
changing dimensions, tested marts. Snowflake and Azure Synapse in production;
DuckDB when a model should build in CI without credentials.
🛡
Guard it
Data contracts that fail the build, column-level lineage, and PII
classification that follows the data downstream instead of going stale
three months after somebody wrote it down.
Research implementations
Papers, made runnable
Reference implementations of methods from papers I have co-authored. Each ships an
evaluation protocol that does not cheat and tests that fail when it does.
None of them quote an accuracy figure — they ship no dataset, so any number
would be a re-run on different data or a copy from a paper this code did not produce.
Both are ways of being wrong in public.
green-anomaly-detection · measured
The one exception, because energy accounting is the contribution:
every detector reports its joules and grams of CO₂e beside its detection score.
Produced by scripts/benchmark.py, three seeds, 6000 flows.
Detector
Tier
Avg precision
Joules
AP / joule
mahalanobis
cheap
0.231
0.14
1.62
z-score
cheap
0.147
0.07
2.29
one-class-svm
expensive
0.182
9.77
0.019
isolation-forest-32
moderate
0.132
8.54
0.016
lof
expensive
0.296
38.50
0.008
isolation-forest
moderate
0.132
25.01
0.005
elliptic-envelope
moderate
0.192
148.54
0.001
Mahalanobis reaches 0.231 average precision for 0.14 joules. LOF reaches 0.296
for 38.50 joules — buying those last 0.065 points costs 275× the energy.
Whether that trade is worth taking depends on your alert budget; the point is that
you can now see the trade at all.
Data engineering
The infrastructure half
The patterns I reach for in production, written as libraries with the reasoning in
the docstrings and a test for every claim. All four run their suites in CI on
Python 3.10, 3.11 and 3.12 — no cluster, no credentials.
Experience
Where the work happened
JUN 2026 — PRESENT · REMOTE
Data Engineer
SimanPro Inc.
Batch and streaming ingestion for structured and unstructured corporate data.
Productionised ETL and ELT on Spark, Airflow and dbt; enterprise warehouse and
lake design; governance, lineage and compliance; real-time streaming over
distributed message queues.
AUG 2024 — MAY 2026 · CONNECTICUT
Data Engineer
Capital One
Automated aggregation pipelines in Python, SQL and Airflow feeding regulatory
compliance and risk reporting. Snowflake and Azure Synapse optimisation down to
micro-partitioning. Real-time ingestion and anomaly monitoring on large-scale
card and transaction data. Power BI self-service models for senior stakeholders.
NOV 2020 — DEC 2022
Data Engineer
Mindtree
Python automation frameworks over large historical datasets, cutting processing
overhead by 40%. Enterprise SQL ETL, views and stored procedures serving
cross-functional analytical layers. Data quality profiling and technical
documentation.
2023 — 2025 · CONNECTICUT
MS, Computer Science
University of New Haven
Preceded by a BSc in Computer Science & Engineering at BRAC University,
Dhaka, with a thesis on voice impersonation detection using LSTM-based RNNs and
LIME explanations.