I design and build data platforms from raw events to trusted warehouse — orchestration, quality gates, modeling — then put n8n and LangChain agents on top of them.
Also teaches data engineering to 29,283 students on Udemy — see the course ↓
Not a generalist list — this is the specific stack I've shipped in production, repeatedly.
Architecting the full path from raw ingestion to governed, trusted data — data lake, warehouse, catalog, and lineage as one coherent platform, not disconnected tools.
Standing up production-grade Airflow — custom operators and plugins, DAG authoring standards, code-review conventions, and on-call runbooks that scale as teams grow.
Building in-house data quality services from scratch — automated checks that catch schema drift, null violations, and SLA breaches before they hit downstream consumers.
Designing dimensional models and star schemas built for fast, trustworthy BI — the layer between raw pipelines and the dashboards people actually make decisions from.
Building agentic AI applications with n8n and LangChain directly on top of warehouse and event-stream data — RAG assistants, natural-language query agents, and automation workflows.
Moving traditional, on-prem data warehouses to modern cloud platforms — replatforming schemas, pipelines, and workloads with minimal downtime and validated parity.
Mentoring engineers and small teams on data platform fundamentals and applied AI — architecture reviews, career guidance, and hands-on pairing, one-on-one or in groups.
Review your current data platform, pipelines, and any existing AI workflows. Come out with a scoped, prioritised plan.
Hands-on delivery — platform architecture, Airflow setup, data quality server, warehouse models, or the n8n/LangChain agent itself.
Runbooks, DAG standards, and a short support window so your team can own and extend what's built.
Architecture reviews and hands-on build support across any of the five core areas — scoped as a fixed project or ongoing advisory.
Discuss a project →A structured report on how your data/AI stack and technical presence hold up against current market standards, with a prioritised roadmap.
Request an audit →End-to-end delivery: pipelines, Airflow platform, data quality server, or warehouse migration — scoped and shipped.
Scope a build →I teach data engineering to a global student base alongside consulting work.
Build ETL pipelines with real-world projects, step by step.
Employer and client names withheld where engagements are proprietary — full context available on request.
Unified BI, AI, and operational workloads onto a single governed AWS data lake, cutting infrastructure costs 60%.
Built a Flask-based data quality platform running 800+ automated checks a day across ingestion and transformation layers.
Shipped a natural-language BI assistant over the warehouse using an agentic n8n workflow, LangChain, and a vector database.
Built the data warehouse powering executive dashboards, plus an Airflow DAG generator framework to automate pipeline creation.
Tell me a bit about what you're working on and I'll follow up within a couple of days.
andalib.info@gmail.com