I build the full path from raw events to a trusted warehouse - orchestration, quality gates, modeling - then wire agentic AI on top of it
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.
Turning raw review, social, and GBP data into a monthly scored report that tells health SMEs exactly what their digital presence is costing them - competitor-benchmarked, AI-written commentary, delivered as a PDF report
A short call to understand your data platform, pipelines, or digital presence — and agree what a successful engagement looks like.
Hands-on delivery — platform architecture, Airflow setup, a data quality server, warehouse migration, an AI agent, or your first Digital Presence Audit report.
Documentation and runbooks for technical builds, or a walkthrough of your action plan for audit clients — plus a short support window either way.
Architecture reviews and hands-on build support across any of my core areas of expertise — scoped as a fixed project or ongoing advisory.
Discuss a project →End-to-end delivery: pipelines, Airflow platform, data quality server, or warehouse migration — scoped and shipped.
Scope a build →A monthly report that finds the fixable gaps in your Google listing, reviews, and social media - ranked by what they're costing you in lost patients, with a clear action list to close them.
Request an audit →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 1500+ automated checks a day across ingestion and transformation layers.
Built a production RAG assistant that lets analysts ask the warehouse questions in plain English - orchestrated with n8n, reasoning handled by LangChain, semantic search backed by 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.
hello@andalibansari.com