Onboarding Guides For Analysts

Why this matters

Strong onboarding guides turn new analysts into confident contributors in days, not weeks. As an Analytics Engineer, you reduce repetitive support, standardize queries, and protect data quality by giving newcomers a clear path to access, core datasets, metrics, and safe query patterns.

• Reduce time-to-first-insight and time-to-first-PR.
• Lower Slack noise with self-serve answers and recipes.
• Prevent data misuse by documenting joining keys, filters, and gotchas.
• Improve consistency across dashboards and analyses.

Concept explained simply

An onboarding guide is the fast lane for new analysts: how to get access, what to look at first, and how to run correct, performant queries. It is not a full data dictionary; it is a practical starter kit.

Mental model

• Runway: Access + environment (accounts, tools, connections).
• Lighthouse: What matters (core datasets, canonical metrics, must-know dashboards).
• Toolbox: How to do it safely (query recipes, join keys, performance tips, QA checks).

What a good onboarding guide includes

• Access checklist (DW, BI, repo, tickets) and expected timelines.
• Tech stack map (warehouse, orchestration, modeling, BI) with why each piece exists.
• Core datasets quick reference (names, grain, owners, freshness/SLA, joining keys).
• Business metrics canon (definitions, filters, edge cases, known exclusions).
• Dashboards index (top 5 to trust, when to use each).
• Query recipes (copy-paste starters with comments, parameters to change).
• FAQ + troubleshooting (common errors, permission issues, null spikes).
• Data request process (how to ask for new models/metrics, templates).
• Change log & SLAs (what changed recently, stability expectations).
• Quality guardrails (checks to run before sharing numbers).
• Glossary (team-specific terms, event names, campaign types).
• Contributions (how to propose doc updates; simple PR template).

Worked examples

Example 1: Day 1 Quickstart (Acme Mart)

Goal today: run a safe query and answer “Yesterday’s paid orders and revenue.”

• Access: acme_warehouse (read), acme_bi (viewer). Connection profile pre-configured.
• Datasets to trust: mart.orders (order grain), mart.customers (customer grain).
• Metric: Gross Revenue = sum(order_amount) where order_status = 'paid'
• Recipe:

-- Yesterday's paid orders and revenue
SELECT
  DATE(order_timestamp) AS order_date,
  COUNT(*) AS orders,
  SUM(order_amount) AS gross_revenue
FROM mart.orders
WHERE order_status = 'paid'
  AND order_timestamp >= DATEADD(day, -1, CURRENT_DATE)
  AND order_timestamp < CURRENT_DATE
GROUP BY 1;

Example 2: Dataset page (mart.orders)

• Grain: 1 row = 1 order id
• Freshness: updated hourly (SLA <= 90 minutes)
• Owner: Analytics Eng (data-oncall@acme)
• Primary key: order_id
• Join keys: customer_id (to mart.customers), store_id (to dim.store). Join type: many-to-one.
• Gotchas: Exclude order_status in ('test','fraud'); refunds recorded in mart.refunds.
• Safe starter filters: order_status = 'paid' AND is_internal = false

Example 3: FAQ / Troubleshooting

Step-by-step: Build an onboarding guide (about 90 minutes)

1. Draft the Runway (15 min)
   What to write

   • Access steps with expected SLAs.
   • Tools list and one-liner purpose each.
   • Connection profile example.
2. Map the Lighthouse (20 min)
   What to write

   • Top 5 datasets with grain, owner, freshness.
   • Top 5 dashboards and “use when”.
   • 3 core metrics with formulas and exclusions.
3. Assemble the Toolbox (30 min)
   What to write

   • 5 query recipes (daily sales, cohort retention, top SKUs, active customers, anomaly check).
   • Join keys + safe filters for each dataset.
   • Performance tips (avoid SELECT *, partition pruning, date filters first).
4. Add Guardrails (15 min)
   What to write

   • QA checklist (reconcile to dashboard, null scans, duplicate key checks).
   • FAQ with top 5 issues.
   • Change log & SLAs section.

Exercises

These match the exercises below. Do them in sequence and check your work with the included solutions.

1. Exercise 1 — Write a 1-page Quickstart: Create a concise Day 1 page: access checklist, top datasets, three core metrics, and one query recipe. Aim for clarity over completeness.
2. Exercise 2 — Create a dataset reference: Pick one high-use table. Document grain, owner, freshness, primary key, join keys, safe filters, and 3 “gotchas”.

Common mistakes and how to self-check

• Over-documenting everything: Keep onboarding to “what to do first”, link out to deeper docs if needed.
• Hiding join keys: Put them front-and-center with examples.
• Vague metric definitions: Add filters, exclusions, and edge cases explicitly.
• No quality checks: Provide a 3-step QA before sharing numbers.
• Stale content: Add a change log date and owners; review monthly.

Practical projects

• Onboarding Starter Pack: Produce Runway, Lighthouse, and Toolbox pages for one domain (e.g., Sales).
• Query Recipe Library: Create 10 copy-paste queries with comments and parameters to change.
• Dataset Passport: For 5 key tables, write dataset pages (grain, keys, filters, freshness, gotchas).
• QA Playbook: Build a short, reusable QA checklist used before publishing any metric.

Learning path

1. Map the tech stack and access steps.
2. List core datasets and canonical metrics.
3. Draft quickstart and query recipes.
4. Add FAQs, guardrails, and change log.
5. Run a 15-minute user test with a new analyst and iterate.

Who this is for

• Analytics Engineers who support a team of analysts.
• BI Developers standardizing dashboards and metrics.
• Data Analysts onboarding peers.
• Data PMs documenting product analytics flows.

Prerequisites

• Comfort with SQL and warehouse connections.
• Familiarity with your modeling layer (e.g., dbt) and BI tool basics.
• Understanding of your core business metrics.

Next steps

• Integrate dataset pages with your modeling docs (e.g., auto-generated schemas).
• Automate freshness statuses and add “last validated” badges.
• Expand the recipe library and tag each with “when to use”.
• Schedule a quarterly doc review with owners.

Mini challenge

In 30 minutes, write a 1-page Quickstart for a new domain (e.g., Marketing). Include access steps, two trusted datasets, two metrics with formulas, and one query recipe. Ask a colleague to try it and note where they slow down.

Quick Test

Take the quick test to check your understanding. Anyone can take it for free. Progress and results are saved only for logged-in users.