I build the full path from raw events to a trusted warehouse - orchestration, quality gates, modeling - then wire agentic AI on top of it. I love turning data into insights that help businesses make better decisions, whether that's a production data platform or an automated digital presence audit that tells a business owner exactly where they stand.
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.
Turning warehouse and event data into dashboards, metrics layers, and ad-hoc analysis that actually drive decisions - not just reports nobody opens.
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.
AI-analysed monthly audits that show local businesses how they look on Google, social and AI search, how they compare with competitors, and exactly what to fix first. Each report is tailored to the business.
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