Data & AI 90 days 2-3 hours/day updated 2026-06-01

AnalyticsOps 90-Day Learning Path

Build AnalyticsOps skills in 90 days: analytics engineering with dbt, BI platform operations, metrics-layer governance, dashboard testing, and analytics SLA management at scale.

What AnalyticsOps means

AnalyticsOps applies software engineering and DevOps practices to the analytics delivery lifecycle. It covers the development, testing, deployment, and monitoring of analytics assets — from SQL models and dbt transformations to BI dashboards and metric definitions. AnalyticsOps ensures that analytics outputs are reliable, versioned, documented, and delivered against agreed SLAs, treating dashboards and metrics as products.

Who should follow this path

Analytics engineers building dbt-based data models
BI developers managing Tableau or Looker environments
Data platform engineers supporting analytics infrastructure
Data product managers setting analytics SLAs
Data scientists who want more robust analytics delivery

Prerequisites

Intermediate SQL and data warehouse knowledge (Snowflake, BigQuery, or Redshift)
Basic dbt or analytics engineering experience
Familiarity with at least one BI tool (Tableau, Looker, or Power BI)
Git workflow knowledge
Basic Python or Jinja templating

The 90-day plan

Daily study recommendation: 2-3 hours/day, six days a week. Consistency beats intensity — block the time in your calendar like a meeting.

Days 1–15: Foundation

AnalyticsOps principles and the analytics engineering lifecycle
dbt project structure, models, and modular SQL
Semantic layer and metrics layer concepts
BI platform landscape: Looker, Tableau, Power BI, Metabase
The analytics development workflow (dev/staging/prod)

Outcome: Set up a production-grade dbt project with environment promotion and Git-based version control.

Days 16–30: Core concepts

dbt tests: schema tests, singular tests, and custom macros
Data quality monitoring with Elementary and re_data
CI for analytics: dbt slim CI with GitHub Actions
Impact analysis with dbt state and model selection
Column-level lineage and documentation with dbt docs

Outcome: Implement a dbt CI pipeline with automated testing, impact analysis, and lineage documentation.

Days 31–45: Tools and workflows

Metrics layer with dbt Semantic Layer and MetricFlow
Looker LookML governance and version control
Dashboard testing and visual regression
BI platform administration: caching, permissions, scheduling
Row-level security implementation in BI tools

Outcome: Deploy a governed metrics layer with tested LookML and row-level security policies.

Days 46–60: Hands-on projects

Analytics observability: freshness, volume, schema drift alerts
Monte Carlo or Soda integration for BI health monitoring
SLA definition for dashboards and data freshness
Incident response for analytics outages
Analytics on-call rotations and runbooks

Outcome: Build an analytics observability platform with SLA-based alerting and on-call runbooks.

Days 61–75: Advanced practices

Self-serve analytics enablement and governance
Analytics catalog and discovery with DataHub or Alation
Data democratization patterns and guardrails
Analytics platform cost optimization
BI embedding and analytics-as-a-service patterns

Outcome: Design a self-serve analytics platform with governed catalog and cost visibility.

Days 76–90: Portfolio, interview & certification prep

AnalyticsOps portfolio project
dbt Analytics Engineering certification prep
AnalyticsOps interview questions
Metrics: dashboard adoption, data freshness SLA, query cost
Emerging: AI-assisted analytics, natural language to SQL

Outcome: Ship a portfolio analytics platform project and prepare for analytics engineering interviews.

Weekly outcomes at a glance

Phase	Outcome
Days 1–15	Set up a production-grade dbt project with environment promotion and Git-based version control.
Days 16–30	Implement a dbt CI pipeline with automated testing, impact analysis, and lineage documentation.
Days 31–45	Deploy a governed metrics layer with tested LookML and row-level security policies.
Days 46–60	Build an analytics observability platform with SLA-based alerting and on-call runbooks.
Days 61–75	Design a self-serve analytics platform with governed catalog and cost visibility.
Days 76–90	Ship a portfolio analytics platform project and prepare for analytics engineering interviews.

Tools to learn

dbt (data build tool)
Looker
Tableau
Snowflake
BigQuery
Elementary
Monte Carlo
Apache Airflow
DataHub
Metabase
Power BI
GitHub Actions

Labs to practice

Mini projects

Build a dbt project with CI pipeline, dbt Semantic Layer metrics, and Elementary data observability
Create a Looker-based governed analytics platform with LookML version control and row-level security
Implement a dashboard SLA monitoring system with freshness alerts and incident runbooks

Interview questions to prepare

What is the difference between a data warehouse model and a semantic layer metric?
How do you implement CI for a dbt project in GitHub Actions?
Explain dbt state-based selection and why it matters for large projects.
What is a metrics layer and why is consistent metric definition important?
How do you handle breaking schema changes without disrupting downstream dashboards?
What observability signals would you monitor for a BI platform?
How do you govern self-serve analytics to prevent metric sprawl?
Describe your approach to row-level security in a multi-tenant analytics environment.

Certification suggestions

dbt Analytics Engineering Certification — dbt Labs
Snowflake SnowPro Core Certification — Snowflake
Looker Business Intelligence Analyst — Google Cloud
Tableau Desktop Specialist — Tableau/Salesforce

Browse the full certification registry for exam details and official links.

Free resources

// instructor-led option

Prefer live, guided training with mentors and certification support? DevOpsSchool.com runs paid instructor-led programs that pair well with this free path.

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