Accelerate your data pipelines and analytics performance.

I help teams make their data workflows faster and more efficient — without rewriting everything from scratch.

Focus areas: Python (Pandas → Polars) and ClickHouse.

🧩 Data Flow Optimization (Pandas → Polars)

Goal: Speed up your Python data pipelines and reduce resource usage.

Typical problems I solve:

• Long ETL jobs that take hours to complete.
• High RAM usage when processing small or medium datasets (1–20 GB).
• Non-vectorized logic or excessive groupby/merge operations.
• Legacy Pandas code that can’t scale further.

What we’ll do:

1. Review your current scripts or notebooks.
2. Measure RAM usage and execution time.
3. Suggest targeted improvements.
4. Provide before/after benchmarks.

Deliverables:

📄 Optimization report + code snippets

🕒 60-min consulting session (remote or on-site in Madrid)