Analyst Onboarding Checklist & 30–60–90 Plan

Welcome — what this journey helps you do

This 30–60–90 onboarding journey is designed to get new analysts contributing to decisions faster by combining practical checklists, sequenced learning milestones, sample first projects, and a simple mentor assessment rubric. Use it as a repeatable baseline and tailor the deliverables, timelines, and data access items to your local KPIs, SLAs, and tooling.

How to use this plan

Copy and adapt this journey for each hire. Owners: assign a mentor and a hiring manager, confirm access to required systems before Day 1, and pick a first/second project that delivers an early stakeholder win. Mentors: run weekly check-ins and use the rubric to surface gaps and learning priorities.

Week 1 (Day 1–7) — access, orientation, and context

Goal: make the analyst comfortable with environment, people, core terms, and first simple tasks.

• Access & environment checklist
  • Company email, chat, calendar, SSO/identity
  • BI tool(s) access (dashboard viewer + edit if appropriate)
  • Data warehouse credentials and read-only queries environment
  • Source system documentation and example extracts
  • Access to ticketing/issue tracker and stakeholder contact list
• Paired sessions: schedule paired sessions with mentor for environment setup, sample queries, and dashboard walkthroughs.
• Core glossary & data model: review core KPIs, data dictionary, and the team’s canonical data model (or a simplified starter view).
• First small task: run a safe read-only query and reproduce a simple, existing chart or report.
• Meet stakeholders: 15–30 minute meet-and-greets with product/ops/finance/marketing owners of primary reports.

30-day (end of month 1) — contribute to production & learn the business

Goal: complete one small production deliverable, participate in decision meetings, and demonstrate basic end-to-end understanding.

• Deliverable: ship a small change or a new report to production (could be a filtered view, corrected metric, or documentation update).
• Attend and participate in one KPI/board/ops review; explain one metric in plain language.
• Document one data lineage from source to KPI and share with the team.
• Complete a short checklist on data quality traps (missing values, duplicates, late arrivals) and remediation steps.
• Begin building an ongoing list of questions and assumptions about the business domain.

60-day (end of month 2) — deeper ownership and analysis

Goal: own a recurring report or dashboard and deliver an independent analysis that answers a stakeholder question.

• Take ownership of one recurring report: SLA, update cadence, owner contacts, and failure modes documented.
• Complete a focused exploratory analysis: identify 2–3 hypotheses, test them, and summarize findings in a short memo or slide deck.
• Propose one small operational improvement or visualization change backed by evidence or stakeholder feedback.
• Demonstrate basic data modeling tasks: join logic, key selection, aggregation correctness, and simple transformations.

90-day (end of month 3) — independent project & stakeholder presentation

Goal: deliver an independent analysis project that informs a decision and demonstrates readiness to act with limited supervision.

• Independent project: pick a meaningful stakeholder problem, execute analysis, and present recommendations in a 15–30 minute stakeholder session.
• Measure outcome: define a simple success metric for the project and a plan to track it for 30–90 days.
• Knowledge capture: add final documentation to the team’s living playbook (data definitions, queries, dashboards, and lessons learned).
• Career conversation: discuss next role expectations and development plan with manager.

Sample first projects (pick one or adapt)

• Reconcile a production KPI: find differences between two reports and fix the root cause.
• Short-run cohort analysis: measure first-week retention for a recent product change.
• Operational alert tuning: reduce false positives in a monitoring rule and document changes.

Mentor rubric & skills checklist (use as a 1–5 assessment)

Rate the analyst from 1 (needs coaching) to 5 (independent) in each area. Use the rubric to focus weekly coaching conversations.

• Technical: queries & data modeling — can run, debug, and explain queries; understands joins, keys, and aggregations.
• Reporting & visualization — creates clear visuals, selects appropriate charts, and explains trade-offs.
• Business understanding — can explain top KPIs and how they relate to stakeholders' goals.
• Data quality & testing — identifies anomalies, documents checks, and suggests fixes.
• Communication & storytelling — writes concise summaries and presents findings to non-technical audiences.
• Process & governance — follows deployment protocols, documents artifacts, and understands data ownership.

Example scoring guidance: 1–2 needs more pairing and small guided tasks; 3 shows independent execution with review; 4–5 contributes proactively and mentors others.

Practical templates & artifacts to include

• Access checklist (pre-Day 1 verification)
• Data lineage template (source & transforms)
• Report ownership card (owner, cadence, SLAs)
• Project brief template (question, data sources, hypotheses, success metric)
• Meeting agenda for 1:1 mentoring and demo sessions

Tailor to local needs

Important: adapt timelines, data access controls, and deliverables to local governance, security, and compliance requirements. Map the plan to the team’s KPIs and use local examples for exercises.

Quick assessment (use weekly)

Simple weekly check: what did you learn this week? What blocked you? What will you do differently next week? Capture answers in your onboarding journal and discuss with your mentor.

Next steps for teams adopting this journey

• Pre-fill the access checklist and confirm accounts before Day 1.
• Assign a mentor with a manageable bandwidth and clear responsibilities.
• Choose first projects that deliver visible stakeholder value within 30–90 days.
• Record rubric scores and plot progress across the first 90 days to identify patterns and training opportunities.

Use this journey as a living structure — keep what works, remove what doesn’t, and add local artifacts so each analyst joins with clarity, purpose, and a fast path to meaningful contribution.