Open to Technical BA and Data Engineering roles · San Diego or remote · available now
Technical Business Analyst specialized in data engineering
I turn ambiguous business requirements into specifications engineers can build — then build the pipeline myself. Python, SQL, Snowflake, dbt, BigQuery, Power BI.
Four builds, end to end: the requirement behind each one, the pipelines, models and definitions that shipped, and the business outcome it moved.
−45% manual prep
Designed Python + SQL ELT on Snowflake and dbt that pulls 8+ source systems into a single analytics layer — 500K+ records a day landing tested, standardized, and ready for Product and Growth.
Data quality: Automated validation rules raised dashboard accuracy from ~88% to 98%
Python · SQL · Snowflake · dbt
Read the case study ↓2 days → 4 hrs
Built reusable Power BI semantic models and DAX measures on top of the analytics layer, so every standard KPI comes from one definition — and teams spin up new dashboards in under a day.
Data quality: Models sit on the validated layer, so 98% dashboard accuracy carries through
Power BI · DAX · Snowflake · dbt
Read the story ↓3–4 wks earlier
Built anomaly-detection and risk models over financial and credit data at a capital firm, and optimized the BigQuery warehouse behind them — ~45% faster workloads powering executive BI.
Data quality: Documented lineage and KPI logic — analytics self-service up ~60% in six months
Python · XGBoost · BigQuery · SQL
Read the story ↓+60% self-service
Documented data lineage, business definitions, and KPI logic across a credit and lending business, tuned the BigQuery models underneath, and trained staff to answer their own questions instead of queueing for an analyst.
Data quality: One definition per metric, owned and versioned — so two teams stopped reporting different truths
BigQuery · SQL · Data modeling · Documentation
Read the story ↓Data for Product and Growth lived in 8+ source systems. Every report meant hand-pulled exports and hours of prep, dashboards hovered around 88% accuracy, and the weekly reporting cycle swallowed about two days — with rework every month when numbers didn’t line up.
The fix wasn’t another dashboard — it was consolidation: land every source in Snowflake through one ELT path, model it once, and give every team the same numbers. I designed the pipelines in Python and SQL to pull all 8+ systems into a single analytics layer.
Extraction and loading run in Python and SQL; transformations live in dbt, where models are versioned, documented, and reviewed like code. The models standardize KPIs across Product and Growth — 500K+ records a day landing analytics-ready instead of analyst-shaped.
Automated data-quality and validation rules run inside the ELT — so bad data is caught in the pipeline, not on a dashboard. Accuracy climbed from ~88% to 98%, and monthly-report rework dropped 30%.
With Power BI semantic models and DAX measures on top of the trusted layer, the weekly reporting cycle fell from ~2 days to ~4 hours, teams spin up new dashboards in under a day, and manual data-prep is down 45%. The numbers stopped being an argument.
Before: manual pulls · ~88% accuracy · 2-day cycle After: automated ELT · 98% accuracy · 4-hour cycle −45% manual prep
Jun 2025 — Jan 2026
Python · SQL · Snowflake · dbt · Power BI
Sep 2024 — Jun 2025
Python · SQL · ETL
2020 — 2023
Python · BigQuery · SQL · BI
2024
Capstone (AbbVie-sponsored): Python + SQL ETL over 1M+ healthcare records into BigQuery data marts, with XGBoost predictive and anomaly-detection models for executive analytics.
2021
Baku, Azerbaijan.
No proficiency bars — proof instead. Chips link to the project where the tool earned its place.
The same work read through the analyst lens: stakeholder translation, business definitions, dependency mapping, and a full spec work sample from a credit-risk build, mapped row by row against what the job asks for.
See the role fit Try it yourselfToggle the validation rules off and watch accuracy fall. Move the risk threshold and trade lead time against false alarms. Walk an ambiguous ask into a testable spec.
Open the demos
I work in the gap between the people who need a number and the systems that produce it. At my last role I took dashboard accuracy from ~88% to 98% — not with heroics, but by turning business rules into automated validation built into the pipeline itself.
My path ran from financial-risk analysis in Baku, through an M.S. in Business Analytics (STEM) at UC Irvine, into data engineering in San Diego. Business degree, analytics master’s, engineering practice — the through-line is finding out what a number is supposed to mean, then making the system produce it.
Off hours you’ll find me hiking with a thermos of tea and an audiobook queued.
Open to Technical BA and Data Engineering roles · San Diego or remote · available now
I’m actively interviewing and can start immediately. If your team needs someone who can run the requirements and also ship the pipeline, let’s talk.
or write to aysan.h@outlook.com
— Aysan