Data Systems Engineer building SQL-backed workflows, APIs, and reliable data automation.

  • batch pipelines you can run, audit, and re-run.
  • SQL-backed workflows with clear validation paths.
  • APIs and automation around reliable data products.
View systems work
Data toolsAboutContact

Start with Portfolio for data systems work: batch pipelines, feature workflows, monitoring, and APIs.

Use Data tools as secondary evidence: smaller validation-heavy utilities with reproducible exports.

Flagship work

Start with the strongest public data systems work: feature workflows, batch scoring, monitoring, and one secondary data tool that shows validation discipline.

Feature store (mini)
  • pandas
  • pytest
  • FastAPI
  • Batch pipeline

Batch compile turns raw extracts into a versioned feature table under locked column specs and validation rules.

GitHubLive demo
Batch scoring pipeline
  • Python
  • Batch
  • pandas
  • pytest

Batch job processes CSV rows with a fixed preprocessing path and writes score, label, model version, and timestamp on each row.

GitHubLive demo
Data quality & monitoring pipeline
  • pandas
  • pytest
  • Streamlit
  • PSI / KS

Validates each incoming batch against a fixed reference and surfaces shifts in inputs, categories, and prediction patterns.

GitHubLive demo
Data cleaning toolkit
  • Streamlit
  • pandas
  • pytest
  • Parquet / JSON

Upstream prerequisite, not a side utility: downstream data workflows ingest the same reviewed tables.

GitHubLive demo
View portfolio
Secondary data tools

How I Build

1. Start from the system shape
I try to make the shape of the work explicit early: whether the right answer is an API, a batch workflow, a smaller data utility, or a supporting analysis step.
2. Check data and constraints
I pay attention to data quality, failure paths, reproducibility, and the limits of what the inputs can support before leaning on model or automation claims.
3. Build for repeatability
I prefer clear steps, testable logic, and outputs that can be run again under the same rules instead of one-off notebook behaviour.
4. Document the tradeoffs
I try to leave clear boundaries around what is implemented, what is intentionally omitted, and what someone reviewing the work should understand next.

Core stack

PythonSQLData pipelinesBatch processingAPIsFastAPIData validationWorkflow automationDockerpytestExcel / Sheets for structured inputs

Open to Roles

If you are hiring for data systems, data engineering, automation, API, or batch-workflow roles, feel free to reach out. Portfolio is the main evaluation path; Data tools is secondary evidence for smaller utilities and validation-heavy tooling.

Contact about roles
© 2026 Vahdettin Karataş. All rights reserved.
Data systems, APIs, batch workflows, and reproducible automation.