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Getting Started

The Data Team works across Majority and Rebtel. This page is the onboarding path. Your manager assigns you a buddy who guides you through it.

Track Your Progress

Your manager creates an onboarding project in Linear with one issue per step below. You own the project and your buddy reviews it.

Manager Checklist

On day 1:

  1. Buddy assigned
  2. Onboarding project and issues created in Linear
  3. First-week meetings booked: buddy intro and team rituals
  4. Company accesses granted, see Majority or Rebtel
  5. Invited to the Slack channels, the Data team in Linear and the GitHub org

1. Shared Setup

  1. Company email and Google account set up
  2. Added to the Data team in Linear
  3. Joined the Slack channels in Communication & Planning
  4. Tools installed, see Development Environment
  5. Terminal set up, see CLI Tools
  6. Anything out of date in these docs fixed as you go

2. Company Setup

  1. Microsoft account for Azure access
  2. Access requests submitted, see Initial Accesses
  3. dbt set up, see dbt Setup
  4. DataGrip connected to BigQuery, see Development Environment
  5. Looker basics learned with the Looker intro
  6. Onboarding topics covered, see Onboarding Topics
  1. Collaboration tools and platform accesses requested, see Initial Accesses
  2. Snowflake user created
  3. dbt connected to Snowflake, see Setup
  4. Onboarding topics covered, see Onboarding Topics

3. Data Engineer Levels

Clear one level at a time. Each level ends with a "done when" check, and each one is an issue in your onboarding project. From Level 4, your buddy picks a real ticket for each level, e.g. updating DAGs to a new credential or adding a new ingestion source.

Level 1: Get Oriented

Done when

You can explain how data gets from a source to a dashboard.

Level 2: Update the Docs

  • Fix or improve something in these docs that was missing or wrong for you, and take it through the PR flow, see GitHub & Version Control

Done when

Your docs PR is merged.

Level 3: First Model

Done when

You can show the model's output in your dev schema.

Level 4: Pipelines

  • Majority: read and trigger an Airflow DAG, see Airflow, and read File Ingestion
  • Rebtel: find a source's Lambda in AWS and its logs

Done when

You have found a pipeline run and its logs, and can say what it loads.

Level 5: Infrastructure

  • Majority: walk through dt-gcp-infrastructure with your buddy, see Infrastructure as Code
  • Rebtel: walk through the AWS account with your buddy, see AWS

Done when

You can point to where a table or Lambda you know is defined.

Level 6: Ship It

  • Pick a minor dbt change and take it through the full PR flow, see GitHub & Version Control
  • Meet the stakeholders of your first tasks

Done when

Your first dbt PR is merged.

Level 7: Own It

  • Join the support rotation and handle requests in your company's data team Slack channel
  • Take ownership of a domain or pipeline with your buddy's help

Done when

You have shipped a change in the area you own.

4. Analyst Levels

Clear one level at a time, like the Data Engineer levels.

Level 1: Query the Warehouse

Done when

You can answer a stakeholder question with a query.

Level 2: Explore

  • Explore the Looker explores, see Looker
  • Explore the main Looker explores, see Looker

Done when

You can find the Explore behind a dashboard you use.

Level 3: Ship a Report

  • Build a first Looker look with your buddy and share it with the team

Done when

A stakeholder is using your report.

Need Help?

  1. Check the relevant documentation section
  2. Ask in your company's data team Slack channel (see Communication & Planning)
  3. Reach out to your buddy, manager or team members