You will get GDPR-Compliant AI Data Pipeline
Project details
The EU is rewriting the rules for AI right now. Their definitions, deadlines, and what counts as personal data are all in motion. For a startup moving fast, it is easy to fall behind on this.
I help startups keep their data pipelines compliant as they scale. Most teams ship first and think about their users’ privacy later (if they think about privacy at all), and personal data ends up sent to third-party AI tools, left in logs, or stuck without a clean deletion path. I help identify those gaps and close them before they become problems.
What our collaboration looks like depends on what’s needed in your exact case: a PII and GDPR-exposure audit of your pipeline with a prioritised fix list, a redaction layer that strips personal data before it reaches an LLM, with audit logging, or a full privacy-by-design pipeline delivered with a GDPR rationale document that your DPO can sign off on.
I’m a data analytics engineer, not a checklist consultant. The pipelines are built (Airflow, dbt, ClickHouse, LLM APIs), and the documentation is written to match, so you get something that works in production and holds up to review.
This is engineering and documentation support, not legal advice.
I help startups keep their data pipelines compliant as they scale. Most teams ship first and think about their users’ privacy later (if they think about privacy at all), and personal data ends up sent to third-party AI tools, left in logs, or stuck without a clean deletion path. I help identify those gaps and close them before they become problems.
What our collaboration looks like depends on what’s needed in your exact case: a PII and GDPR-exposure audit of your pipeline with a prioritised fix list, a redaction layer that strips personal data before it reaches an LLM, with audit logging, or a full privacy-by-design pipeline delivered with a GDPR rationale document that your DPO can sign off on.
I’m a data analytics engineer, not a checklist consultant. The pipelines are built (Airflow, dbt, ClickHouse, LLM APIs), and the documentation is written to match, so you get something that works in production and holds up to review.
This is engineering and documentation support, not legal advice.
AI Development Type
Software MaintenanceAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$700
|
Standard
$1,400
|
Advanced
$2,200
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 18 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | |
Detailed Code Comments | - | - | |
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | - | ||
Taxonomy | - | - | - |
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Additional Revision
+$150About Yulia
Data Engineer GDPR-Compliant AI Pipelines dbt Airflow Python AWS
Berlin, Germany - 6:17 am local time
The problem I solve: a team wants to feed user data into an LLM but can't legally send raw personal data to third-party APIs. I build the layer in between. PII redaction, frequency aggregation, identifier replacement - the feature ships without regulatory risk.
Most recent example: built a privacy-preserving OpenAI pipeline in production. On-premise pattern extraction kept sensitive processing in-house; aggregated frequency data (not individual records) went to the API; PII was stripped via Python + Google Cloud DLP before any external call. Outcome: GDPR-defensible AI feature with a documented data minimisation rationale.
Outside of privacy work, I handle standard data engineering: ETL pipelines (Shopify/Stripe/APIs → BigQuery/Redshift/ClickHouse), dbt projects (models, tests, docs, CI), Airflow DAGs, and Metabase dashboards.
I take fixed-price projects (1–2 weeks) and prefer clearly scoped work with a defined deliverable. I don't do hourly retainers on vague scope.
Stack: Python · SQL · dbt · Airflow · AWS (S3, IAM, SQS, EC2) · Redshift · BigQuery · ClickHouse · Metabase · OpenAI API · Google Cloud DLP
Steps for completing your project
After purchasing the project, send requirements so Yulia can start the project.
Delivery time starts when Yulia receives requirements from you.
Yulia works on your project following the steps below.
Revisions may occur after the delivery date.
Scoping & review
I review your pipeline, data, and existing materials, confirm the scope for your chosen tier, and agree on the GDPR areas to focus on.
PII & data-flow audit
I map how personal data moves through your system, pinpoint where PII is exposed to a third-party LLM or API, or is at rest, and note the gaps.