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Snowflake Data Engineering Program

The complete Snowflake syllabus plus the ecosystem every real project needs: Python, dbt, AWS and Airflow, finished with four end-to-end live industry projects. Designed and taught by Jitendra Chauhan.

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14Snowflake domains
140+Snowflake topics
4Crash courses: Python, dbt, AWS, Airflow
4Live projects: Retail, BFSI, Healthcare, Logistics

Who should take this course?

If any of these sounds like you, this program was built for you.

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Snowflake syllabus

14 domains, from architecture to Cortex AI

Every topic is taught hands-on in a live Snowflake account, from the fundamentals interviewers test first to the advanced features senior engineers use in production.

FoundationalData handlingEngineeringDevelopmentSecurity & admin

Core domains

Advanced domains

The journey

From zero to job-ready Snowflake data engineer

One connected path: learn the language, master the platform, add the tools every data team uses, then build and defend real production-grade projects.

1

Python foundations

Python for data engineers and Snowflake automation

2

Snowflake core

Architecture, SQL, loading, performance, Time Travel, security

3

Snowflake advanced

Streams, tasks, dynamic tables, Snowpark, sharing, governance, Cortex AI

4

Modern data stack

AWS for Snowflake, dbt Core, Apache Airflow

5

Live projects

4 end-to-end, organisation-level projects in 4 domains

6

Certify & interview

SnowPro Core prep, project walkthroughs, scenario questions

Ecosystem crash courses · 10 classes

Everything around Snowflake that real projects need

No company runs Snowflake alone. These focused crash courses cover exactly what a Snowflake data engineer uses every day, and nothing that wastes your time.

PY
3 classes

Python for Snowflake

From "never coded" to automating Snowflake loads, Snowpark pipelines and dashboards.

Class 1 · Python essentials for data engineers

  • Variables, data types, lists, dicts, sets, comprehensions
  • Functions, modules, pip and virtual environments
  • File handling: CSV, JSON, YAML config, os / pathlib
  • Exceptions, logging, datetime

Lab: parse and validate nested order JSON files

Class 2 · Python ↔ Snowflake ↔ AWS

  • snowflake-connector-python, key-pair auth, bind variables
  • pandas: fetch_pandas_all, write_pandas
  • boto3: upload to S3, list and move files
  • Reusable, config-driven loader with audit logging

Lab: automated "file → S3 → COPY INTO → audit" script

Class 3 · Snowpark & automation

  • Snowpark Session and DataFrame API, lazy execution
  • Python UDFs, UDTFs and stored procedures
  • Snowflake CLI deployment, scheduling with tasks
  • Streamlit in Snowflake basics

Lab: Snowpark pipeline + Streamlit KPI dashboard

dbt
3 classes

dbt Core crash course

The industry-standard way to build, test and document SQL transformations on Snowflake.

Class 1 · Setup & modelling

  • dbt Core + dbt-snowflake, profiles.yml, project layout
  • sources, models, ref() and the DAG
  • Materialisations: view, table, incremental, ephemeral
  • Staging → intermediate → marts layering

Lab: retail star schema built 100% in dbt

Class 2 · Quality, reuse & history

  • Generic and singular tests, dbt_utils, dbt_expectations
  • Snapshots for SCD Type 2, seeds
  • Jinja, macros, packages, variables
  • Incremental strategies: merge, delete+insert, dynamic tables

Lab: customer SCD2 snapshot + 25 data tests

Class 3 · Production dbt

  • dbt docs, lineage graph, exposures
  • Tags, selectors, dev / prod targets
  • Git branching and pull-request workflow
  • GitHub Actions slim CI (state:modified+)

Lab: CI pipeline that tests every pull request

AWS
2 classes

AWS services for Snowflake

Only the AWS you need to land, secure, trigger and stream data into Snowflake.

Class 1 · Secure data lake integration

  • IAM users, roles, policies, trust and external ID
  • S3: buckets, prefixes, versioning, lifecycle, KMS
  • Storage integration end to end (no access keys)
  • S3 event notifications, SQS and SNS for Snowpipe

Lab: S3 → Snowpipe auto-ingest in under 1 minute

Class 2 · Event-driven & streaming

  • Lambda triggers calling Snowflake
  • Kinesis Data Firehose → Snowflake (streaming)
  • Glue Data Catalog, Athena and Iceberg interoperability
  • Secrets Manager, CloudWatch, PrivateLink concepts
  • EC2 / MWAA for hosting dbt and Airflow, cost basics

Lab: Lambda file validator → quarantine bad files

AF
2 classes

Apache Airflow for Snowflake

Orchestrate S3, Snowflake, dbt and alerts as one reliable, monitored pipeline.

Class 1 · Airflow fundamentals

  • Architecture: scheduler, executor, workers, metadata DB
  • DAGs, tasks, operators, cron, catchup, backfill
  • Connections, variables, XCom, TaskFlow API
  • Local setup with Docker / Astro CLI

Lab: first Snowflake DAG with SQLExecuteQueryOperator

Class 2 · Production orchestration

  • S3KeySensor → load → dbt (Cosmos) → DQ checks
  • Retries, SLAs, failure callbacks, email / Slack alerts
  • Dynamic task mapping, pools, deferrable sensors
  • Amazon MWAA; Airflow vs Snowflake tasks and when to use which

Lab: full daily pipeline DAG with alerting

Live industry projects · 4 domains

Build what real data teams build

Each project starts from a business problem, runs on real AWS and Snowflake infrastructure with messy, realistic data, and ends with a working product you can demo and explain in any interview.

01Retail & e-commerce

ShopSphere: omnichannel sales intelligence

An Indian retailer with web, app and 15 stores wants one platform for near real-time sales, customer 360 and partner reporting, compliant with the DPDP Act.

Data sources

CRM customers (CSV)Product catalog (Parquet)Orders (nested JSON)Clickstream (NDJSON)Product reviews

Pipeline

S3 landing→Snowpipe→RAW (VARIANT)→Streams + Task DAG→SCD2 star schema→Dynamic tables→Streamlit + partner share

What you implement

  • Schema inference and schema evolution
  • SCD1 + SCD2 with MERGE
  • Late-arriving facts and dimensions
  • Masking, row access by region
  • Multi-tenant partner data share
  • RFM segments, cohorts, funnels
  • Time Travel recovery and zero-copy QA clone
  • Alerts, data metric functions, cost monitors
Orders visible in ~1 minPII masked by roleZero-copy partner sharing
02Banking & FinTech

PayGuard: real-time card fraud & regulatory reporting

A digital bank must flag suspicious card transactions within seconds, keep an auditable history of every customer change and produce daily regulatory reports.

Data sources

Card transactions streamCore banking customers (CDC)Accounts & cardsMerchant masterFX rates API

Pipeline

Kinesis Firehose→Snowpipe Streaming→Data Vault 2.0 (dbt)→Fraud rules + ML anomaly→Alerts→Regulatory marts & exports

What you implement

  • Hubs, links and satellites with dbt
  • Velocity and geo-impossible fraud rules
  • MATCH_RECOGNIZE spending patterns
  • PCI-DSS card number tokenisation
  • Branch-level row security
  • External API enrichment (FX)
  • Daily regulatory report unload to S3
  • Airflow SLAs and failure alerts
Fraud flagged in secondsFull audit historyAutomated compliance reports
03Healthcare & insurance

CareClaims 360: payer claims & member analytics

A health insurer needs trusted claims, eligibility and provider analytics to cut claim turnaround time and detect fraud, waste and abuse, while protecting patient data under HIPAA.

Data sources

Medical claims (FHIR-style JSON)Member eligibilityProvider directoryPharmacy claimsScanned claim PDFs

Pipeline

S3 + SFTP drops→Airflow→COPY / Snowpipe→dbt models + snapshots→Claims marts→Provider share & dashboards

What you implement

  • Semi-structured claim parsing at scale
  • Eligibility history with dbt snapshots
  • PHI auto-classification and masking
  • Minimum-group-size privacy rules
  • Claim denial and turnaround KPIs
  • Outlier billing (fraud, waste, abuse)
  • Extract fields from claim PDFs with AI
  • Secure provider data sharing
HIPAA-grade governanceClaim TAT & denial insightsFWA outlier detection
04Supply chain & logistics

FleetFlow: IoT shipment tracking & demand forecasting

A cold-chain logistics company tracks thousands of trucks. It needs live ETAs, temperature-breach alerts, warehouse demand forecasts and an open lakehouse shared with its Spark team.

Data sources

GPS & temperature IoT eventsWarehouse inventory (WMS)ERP sales ordersRoute & depot masterWeather API

Pipeline

IoT → Kinesis→Snowflake (streaming)→Iceberg tables on S3→Geospatial ETA + dynamic tables→ML forecast→Alerts & ops dashboard

What you implement

  • High-volume time-series ingestion
  • Iceberg tables read by Athena / Spark
  • Distance, geofence and ETA with GEOGRAPHY
  • Cold-chain breach alerts in minutes
  • Demand forecasting per warehouse
  • Clustering and search optimisation at scale
  • Cost tuning: warehouses vs serverless
  • Live fleet map in Streamlit
Live ETAs on a mapCold-chain breach alertsDemand forecasts
Every project includes

Proof you can show, not just certificates

Architecture diagramAn end-to-end flow you can draw and defend on a whiteboard.
GitHub repositorySQL, Python, dbt and Airflow code with a clear README.
CI/CD & data testsAutomated tests on every change, the way real teams work.
Working dashboardA Streamlit app on live data to demo in interviews.
Runbook & docsOperating guide, lineage docs and troubleshooting notes.
Interview storyResume bullet points and scenario questions for each project.
Free practice workbook

Brush up your Snowflake SQL first.

12 modules, 200 exercises and 5 real-time projects on realistic practice databases. Work through it before the batch starts, or test where you stand today.

Open the SQL workbook

Ready to start the Snowflake program?

Call or WhatsApp +91 83682 58062 for fees and the next batch date.

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