CI/CD & Deployment~15 hours

Platform Project — Build a Complete Data CI/CD Platform

Multi-Environment CI/CD Platform

Automate the deployment of data infrastructure across Dev, Staging, and Production environments using code, eliminating manual configuration errors.

GIT + ENVBranch & Config

CI PIPELINETest & Validate

DEPLOYBlue/Green + Rollback

PRODUCTIONMonitor & Govern

PipelineOps CI/CD Platform \u2014 Progressive Build

Part 1: Git Strategy & Environment Design

6/24

Part 2: dbt CI Pipelines & Quality Gates

12/24

Part 3: Blue/Green Deployments & Rollback

18/24

Part 4: Production Reliability & Enterprise Scale

LIVE CI/CD Platform

What You'll Build

A complete CI/CD platform for PipelineOps Inc, an e-commerce data platform with 15 engineers across 3 squads (Core ETL, Analytics, ML Platform).

Git Branching Strategy

Data-team-specific branching with pre-commit hooks, Git LFS for test fixtures, and PR templates with downstream impact analysis

trunk-based flow

dbt CI Pipelines

Automated dbt test + build on every PR with slim CI, state comparison, and quality gates that block bad merges

< 5 min CI runs

Blue/Green Deployments

Zero-downtime releases using view abstraction and atomic schema swaps with automated pre-cutover validation

zero downtime

Rollback & Time Travel

Instant rollback with Snowflake time travel and backup schema retention. Recover from any bad deployment in under 60 seconds

< 60s rollback

Terraform IaC

Manage Snowflake warehouses, roles, grants, and resource monitors as code with Terraform modules and state management

50+ resources

Enterprise Governance

Deployment SLAs, incident response runbooks, chaos testing, and audit-ready governance with SOC2-compliant change management

99.9% SLA

Progressive Build Path

Each part builds on the previous. You'll go from Git config to a fully governed CI/CD platform.

Part 13–4 hours

Foundation — Git Strategy & Environment Design

Set up a production-grade Git workflow for data teams, configure dev/staging/prod environments with isolated schemas, implement secrets management, and build pre-commit hooks for data quality.

Git branching strategy for data teamsData-aware .gitignore & Git LFS configPre-commit hooks (dbt compile, lint, secrets scan)Multi-environment dbt profiles (dev/staging/prod)+2 more

6/6 items complete — Environment foundation ready

Part 24–5 hours

Automation — dbt CI Pipelines & Quality Gates

Build GitHub Actions CI pipelines for dbt with automated testing, PR quality gates, slim CI with state comparison, and data quality validation that blocks bad merges before they reach production.

dbt test suite (generic + custom tests)GitHub Actions CI workflow for dbtPR quality gates with status checksSlim CI with state:modified+ strategy+2 more

12/12 items complete — CI pipeline operational

Part 33–4 hours

Deployment — Blue/Green & Rollback Strategies

Implement zero-downtime blue/green deployments for data pipelines using view abstraction, build rollback strategies with time travel, design data backfill workflows, and version Airflow DAGs.

Blue/green deployment with view swapsAutomated pre-cutover validationRollback with Snowflake time travelData backfill strategy & scheduler+2 more

18/18 items complete — Zero-downtime deployments live

Part 43–4 hours

Production — Reliability & Enterprise Scale

Deploy with Terraform IaC, implement SLA monitoring and incident response, build chaos testing for pipelines, and create enterprise deployment governance with audit trails.

Terraform config for Snowflake (warehouses, roles, grants)Dockerized dbt with multi-stage buildsSLA/SLO monitoring with freshness checksIncident response framework & runbooks+2 more

LIVE production CI/CD platform deployed

Total Time: ~15 hours

Download Sample Data

PipelineOps' e-commerce data — 765K+ records across 4 source tables

raw_orders.csv

200K records · 28 MB

E-commerce order transactions for dbt transformation testing

raw_customers.csv

50K records · 8 MB

Customer dimension data for join and quality tests

raw_products.csv

15K records · 3 MB

Product catalog with schema evolution test variants

raw_events.csv

500K records · 45 MB

Clickstream events for incremental pipeline testing

Or generate synthetic data using our Python script

Tech Stack You'll Master

GitVersion Control

GitHub ActionsCI/CD

dbt CoreTransform

SnowflakeWarehouse

TerraformIaC

DockerContainers

KubernetesOrchestration

AirflowScheduling

Great ExpectationsQuality

PythonLanguage

HelmK8s Packaging

Slack APIAlerting

Why CI/CD for Data?

CI/CD is the #1 skill gap between "data engineer" and "platform engineer." Companies like Netflix, Uber, and Stripe require it across Core DE, Senior, Platform, ML, and AI roles.

Multiplier Across All Roles

CI/CD knowledge compounds: Core DE, Senior DE, Platform, ML Engineer, and AI Engineer all need deployment fluency.

Production Reliability

Blue/green deployments, rollback strategies, and quality gates are table stakes at top-tier companies.

Platform Leadership

Designing CI/CD for a 15-person data team proves you can build infrastructure that scales with the organization.

Resume-Ready Portfolio Project

Add these bullet points to your resume after completing the project:

Built end-to-end CI/CD platform for data pipelines with GitHub Actions, dbt slim CI, and automated quality gates reducing deployment failures by 85%
Designed blue/green deployment strategy for Snowflake with view abstraction, achieving zero-downtime releases and sub-minute rollback capability
Implemented Infrastructure as Code with Terraform managing 50+ Snowflake resources (warehouses, roles, grants) across dev/staging/prod environments
Created enterprise deployment governance framework with SLA monitoring, incident response runbooks, and chaos testing for 15-engineer data platform

Completion certificate included

Prerequisites

Git & GitHub Basics

Required

Comfortable with clone, commit, push, pull, branches. Understanding of pull requests.

dbt Fundamentals

Required

Can write dbt models, run dbt build, understand sources and refs. Our dbt skill toolkit covers this.

SQL Proficiency

Required

Comfortable writing DDL and DML. Understanding of schemas, views, and grants.

Docker & Terraform

Helpful

Basic container and IaC experience is helpful. Part 4 teaches from scratch if needed.

Related Learning

→

DataOps CI/CD Learning Path

/learn/cicd-deployment

→

What is DataOps?

/guide/what-is-dataops

Ready to Build Production CI/CD?

Start with Part 1: Git Strategy & Environment Design