Analyst Onboarding Checklist & Practical Exercises

Welcome — Purpose and how to use this journey

This practical onboarding journey helps new analysts become productive faster by combining a 30/60/90 checklist, concrete walkthroughs, hands‑on exercises with evaluation criteria, a mentoring schedule template, an access checklist, and a short 90‑day assessment. Use this as a repeatable baseline: tailor the specific tools, datasets, KPIs, SLAs, and stakeholder names to your organization.

How to apply the journey

• Assign an onboarding owner (mentor) and a local sponsor who needs early analyst outputs.
• Share the journey with the new hire before Day 1 so expectations are clear.
• Run the checklist items as working tasks rather than passive reading. Pair the new analyst with the mentor for at least some tasks.
• Use the exercises as graded milestones: one small deliverable in the first 30 days, a mid‑level analysis by day 60, and an ownership/dashboard task by day 90.

30 / 60 / 90 Day Checklist (practical goals and example tasks)

First 30 days — access, orientation, and first contribution

• Get access: BI tool, data warehouse, data catalog, code repo, Slack, ticketing, shared drives (use Access Checklist below).
• Complete organization orientation: org chart, product lines, customers, core KPIs.
• Walkthrough key artifacts: top 3 dashboards, data catalog entry for main datasets, ETL pipeline diagrams (see Walkthroughs).
• Deliverable: Complete a short "data sanity" exercise (Exercise 1) and present findings to mentor.
• Weekly mentor check‑ins (30 mins) and a sponsor touchpoint to understand expectations.

By 60 days — ownership and collaboration

• Own one recurring report or dashboard: understand the data lineage and business use.
• Participate in at least two stakeholder meetings; take notes; propose one small improvement.
• Deliverable: Mid‑level exploratory analysis (Exercise 2) with a short slide summary and next steps.
• Mentor review of code, queries, and documentation standards.

By 90 days — lead a small project and measure impact

• Lead an improvement: dashboard redesign, report automation, or an operational insight that influences a decision.
• Handoff/knowledge share: document datasets used, key queries, and an owner for ongoing updates.
• Deliverable: Dashboard improvement and testing (Exercise 3) and a 90‑day review with manager and sponsor.
• Complete the 90‑day assessment (skills checklist and manager rating).

Recommended walkthroughs (minimum)

1. Data catalog: find canonical dataset definitions, owners, and freshness SLA.
2. Key dashboards and reports: who uses them, when, and for what decisions.
3. Data pipelines / ETL overview: batch vs streaming, schedules, failure modes.
4. Semantic / metrics layer: canonical KPIs and calculation rules.
5. Governance and access process: how to request new data or request schema changes.
6. Stakeholder map: product/ops/finance owners and their expectations.

Three practical sample exercises (with datasets and evaluation criteria)

Exercise 1 — Data Quality Triage (First 30 days)

Dataset: A recent export of a core transaction table (e.g., orders, events, or claims) with timestamps, identifiers, status codes, and a few numeric fields.

Task: Discover and document the top 3 data quality issues, quantify their scope, suggest likely causes, and propose next steps or quick fixes.

Deliverable: 1‑page brief and a short 10‑minute presentation to the mentor.

Evaluation rubric (score 0–5 each):

• Problem identification: accuracy of issues found.
• Impact estimation: reasonable quantification of scope.
• Root cause hypothesis: plausible and connected to pipelines or sources.
• Actionability: clear next steps and owners suggested.

Exercise 2 — Exploratory Analysis (By 60 days)

Dataset: 6 months of product/performance metrics with user segments, geography, and returns/cancellations.

Task: Identify top drivers of a metric (e.g., returns rate, wait time, churn) and propose two data‑driven experiments or operational fixes.

Deliverable: 5‑slide summary with visuals, one appendix showing key queries/analysis code.

Evaluation rubric (score 0–5 each):

• Clarity of signal: are the findings supported by data and visuals?
• Analytical approach: appropriate methods and assumptions explained.
• Business sense: recommendations tied to stakeholder impact.
• Reproducibility: queries/code are readable and runnable.

Exercise 3 — Dashboard Improvement & Testing (By 90 days)

Dataset: the live metrics used by a weekly operational dashboard.

Task: Propose and implement one meaningful improvement that reduces ambiguity or speeds a common decision (e.g., better filters, clarified definitions, an alert). Document how you tested the change and measured improvement.

Deliverable: before/after screenshots or prototype, brief test plan, and initial impact indicators.

Evaluation rubric (score 0–5 each):

• Design clarity: improved usability for the target user.
• Technical soundness: correct metrics and handling of edge cases.
• Testing: reasonable test plan and early evidence of improvement.
• Stakeholder adoption: communicated and trained users or handed off to owner.

Mentoring & meeting cadence (template)

• Weekly 1:1 with mentor (30–45 mins): progress, blockers, code reviews.
• Biweekly sponsor check (15–30 mins): alignment on priorities and expectations.
• Monthly cross‑functional review: present exercise deliverables and solicit feedback.
• End of 90 days: formal review with manager, mentor, and sponsor using the assessment template below.

Access checklist (minimum set)

• Sign‑on to BI tool (e.g., Looker/Power BI/Tableau) + viewer/editor rights as appropriate.
• Credentials for data warehouse and query tool (read only initially, escalate when needed).
• Data catalog access and training account.
• Code repository (git) and sandbox workspace (Jupyter/VS Code) with sample notebooks.
• Project management/ticketing tool and team communication channels (Slack/Teams).
• Documentation drive and style guide (SQL/query standards, naming conventions).

90‑day assessment template (competencies & measures)

Use a short skills checklist with manager ratings (0–4) across these domains, plus a short narrative on impact and next steps:

• Technical: SQL/data modeling, visualization, reproducible analysis.
• Business: domain knowledge, stakeholder understanding, decision framing.
• Communication: clarity of insights, storyboarding, stakeholder engagement.
• Collaboration: responsiveness, handing off, following team standards.
• Delivery: completed exercise scores, ownership of a report/dashboard, time to first independent task.

Suggested measurable goals: time to first independent ticket closed, number of stakeholder meetings led, average exercise rubric score, and evidence of one measurable impact (e.g., reduced manual time, clearer decision on X).

Tailoring guidance and common pitfalls

Adapt dataset names, KPIs, templates, and access steps to local tooling and governance. Avoid common mistakes such as tool‑first onboarding, leaving new hires to learn ad‑hoc, or failing to connect exercises to real stakeholder decisions. Keep mentoring time protected — scheduled 30 minutes a week is a high‑leverage investment.

Next steps

Copy this journey into your domain, attach local datasets and the canonical metrics definitions, and convert the checklists and assessment into interactive forms (optional) so you can capture progress and compare cohorts over time.