Claude for Data Engineers — Ship Real Data Projects

A data project that used to take a team months, you'll deliver alone in days. This course teaches you to run Claude the way a senior consultant does — from a client's Statement of Work all the way to a monitored production deployment — using real warehouses, real code, and real guardrails.

This is not a prompt-tips course. Across 28 modules and 113 lessons you work a single running client — Northwind Retail — from blank SOW to shipped pipeline. You'll drive every Claude API capability that matters for data work, turn natural language into cost-aware SQL on Snowflake and Databricks, generate and review dbt / PySpark / Airflow pipelines, build production agents with the Claude Agent SDK, wire your stack together with MCP, and finish by delivering an end-to-end project as a certification capstone.

What makes this course different:

• Project-driven, not feature-driven. One real client (Northwind Retail) runs through the whole course — SOW → requirements → design → build → test → deploy → operate.

• Code-first and warehouse-real. Real SQL on Snowflake and Databricks, real dbt models, PySpark transforms, and Airflow DAGs — with validation and cost guardrails, not toy snippets.

• Built on the 2026 Claude stack. The messages API, streaming, structured outputs, prompt caching, tool use, the Claude Agent SDK, the native memory tool, context editing, and MCP — taught by building, not by slideware.

• Senior-engineer review reflexes. Every generation is followed by a "review it like a senior" pass — anti-pattern galleries, self-correcting queries, generated tests, and data contracts.

• Ship-it mindset. Monitoring, alerting, incident triage, FastAPI services, SSE streaming, cost control at data scale, threat-modeling, prompt-injection-via-your-data, PII and least privilege.

• Zero hallucination. Current model IDs, real API surface, real libraries — Snowflake, Databricks, dbt, PySpark, Airflow, Pydantic, the Anthropic SDK.

The arc you'll work: a fast first win in Claude Code, three "transformation" wins (schema→SQL, failure-log→fix, report→insight), the context stack and prompt caching that make Claude both cheaper and better, Claude Code as your daily driver for repo-wide refactors / migrations / tests / docs, production NL-to-SQL and an analytics agent on Databricks, pipeline generation across dbt + PySpark + Airflow, structured extraction of docs and invoices into tables, auto-documentation + column-level lineage + data contracts, data-quality and 3 AM failure triage, RAG over your own data with Cortex Search and Databricks Vector Search, tool use that gives Claude hands on the warehouse, agents built the 2026 way with the Claude Agent SDK, long-running memory and context compaction, multi-agent ingestion pipelines, MCP servers that connect Claude to your stack, the full SOW→plan→design→build→deploy→operate consulting workflow, production APIs with FastAPI and streaming, cost control and observability, AI-on-data security and governance, and a certification capstone where you deliver Northwind end-to-end.

The capstone (your proof of skill): you take the Northwind Retail brief and deliver the complete project — architecture, data model, ingestion, transformations, serving layer, tests, data-quality suite, orchestration, and a monitored deployment — against a rubric, compressing what used to be months of team effort into days.

Who this is for:

• Working data engineers who want to multiply their output with Claude — not toy demos, real delivery

• Senior / lead engineers and consultants who scope and ship client data projects and want to compress timelines

• Analytics engineers and dbt / Snowflake / Databricks practitioners adding AI to their workflow

• Platform and ML engineers who need production agents, tool use, and MCP done correctly and safely

• Anyone who can already write SQL and Python and wants the consulting-grade, SOW-to-production Claude workflow

By the end of this course, you will be able to take a real data project from a written Statement of Work to a monitored production deployment using Claude — and do it in days, with the review discipline and guardrails a senior engineer brings.

Enrol now. The engineers who win the next two years aren't the ones who use AI for autocomplete — they're the ones who ship whole projects with it. Become one of them.