30/60/90 Day Onboarding Plan

A structured plan for your first 90 days in a new data analyst role. Adapt the specifics to your company, but follow the phases: Learn → Contribute → Lead.

Days 1–30: LEARN

Primary goal: Understand the business, the data, and the people. Resist the urge to "prove yourself" — invest in learning first.

Week 1: Orientation & Context

• [ ] Meet your manager: understand their expectations, communication preferences, and how they define success for your first 90 days
• [ ] Get access to all tools: data warehouse, BI tools, documentation, code repos, communication channels
• [ ] Map the team: Who does what? Who are the stakeholders? Who are the subject-matter experts?
• [ ] Read existing documentation: data dictionaries, past analyses, team wikis, onboarding guides
• [ ] Understand the business model: How does the company make money? What are the key products/services?

Week 2: Data Landscape

• [ ] Explore the data warehouse: What tables exist? How is data organized? What are the key entities (users, orders, events)?
• [ ] Find 3-5 "source of truth" tables and understand their schema, update frequency, and known issues
• [ ] Identify existing dashboards and reports: What does the team already track? What are the key metrics?
• [ ] Run exploratory queries: Verify you can access data, understand column meanings, and spot obvious patterns
• [ ] Document questions and knowledge gaps as you find them (you'll fill these in over the next weeks)

Week 3: Stakeholder Context

• [ ] Schedule 1:1s with 3-5 key stakeholders (product managers, marketing leads, operations): Ask "what questions keep you up at night?" and "what data would be most useful to you?"
• [ ] Attend recurring meetings as an observer (sprint planning, business reviews, standup syncs)
• [ ] Understand the team's prioritization process: How does analytical work get requested? How is it prioritized?
• [ ] Review the last quarter of analytical output: What was produced? What was the quality bar?

Week 4: First Small Contribution

• [ ] Pick one small, well-defined task (a dashboard fix, a data pull, a report update) and deliver it
• [ ] Get feedback from your manager on the output quality and process
• [ ] Draft a "state of understanding" document: What you've learned, what you still don't understand, and initial observations about opportunities
• [ ] Share observations with manager: "I noticed X — is that known? Should I investigate?"

Day 30 Check-In with Manager

Discuss:

• What you've learned about the business and data
• Where you see gaps or opportunities (first impressions are valuable)
• Confirm priorities for Days 31-60
• Any resource/access/training needs

Days 31–60: CONTRIBUTE

Primary goal: Deliver independent value. Take ownership of projects with increasing scope.

Week 5-6: Own a Recurring Deliverable

• [ ] Take over ownership of one recurring report or dashboard
• [ ] Improve it: Fix data quality issues, add missing context, improve visualizations
• [ ] Document the process so it's reproducible (not just in your head)
• [ ] Deliver it on time every cycle without reminders

Week 7-8: First Independent Analysis

• [ ] Propose an analysis to your manager (based on what you heard from stakeholders in Month 1)
• [ ] Scope it: What's the question? What data do you need? What's the timeline?
• [ ] Execute end-to-end: Pull data → Analyze → Visualize → Write up findings → Present
• [ ] Apply the "So What" framework: Every finding needs a business implication and recommended action
• [ ] Deliver to the relevant stakeholder and get their feedback

Throughout Month 2

• [ ] Start building reusable SQL templates for common queries in your domain
• [ ] Identify one process improvement (something manual that could be automated, or a data quality issue that could be monitored)
• [ ] Begin building relationships with cross-functional partners (engineers for data questions, product managers for context)
• [ ] Ask for feedback regularly: "What could I do better?" and "Is this the level of depth you need?"

Day 60 Check-In with Manager

Discuss:

• Your independent analysis: impact, feedback received
• Areas where you're feeling confident vs. areas needing development
• Priorities for Month 3 (what should you OWN going forward?)
• Career development: What skills should you focus on building?

Days 61–90: LEAD

Primary goal: Operate independently, proactively identify valuable work, and begin building your reputation as the domain expert.

Week 9-10: Proactive Problem Identification

• [ ] Identify a business question that nobody asked but should be answered
• [ ] Propose it proactively: "I noticed [pattern] in the data. I'd like to investigate because [potential business impact]."
• [ ] Execute the analysis with full "So What" framework
• [ ] Present findings to stakeholders without your manager in the room (demonstrate independence)

Week 11-12: Build Your Analytical Infrastructure

• [ ] Create a documentation artifact that helps the team (data dictionary update, analysis playbook, FAQ document)
• [ ] Propose a metric or alert that doesn't exist yet (something you'd want monitored based on your Month 1-2 observations)
• [ ] Begin developing a "spidey sense" for data anomalies — check key metrics daily and flag unusual movements
• [ ] Take on a slightly larger scoped project that will extend into Month 4+

Throughout Month 3

• [ ] Build your stakeholder relationships: Become the person they come to for data questions in your domain
• [ ] Start attending meetings as a contributor (not just observer) — add data context to discussions
• [ ] Share learnings with the team (informal knowledge sharing about an analysis approach, a useful SQL pattern, or a business insight)
• [ ] Reflect on your first 90 days and draft goals for the next quarter

Day 90 Summary Document

Write a one-page summary for your manager:

1. What I've learned — Key business and data insights from my first 90 days

2. What I've delivered — Concrete outputs and their impact

3. What I see as opportunities — 2-3 high-value analytical projects for the next quarter

4. What I need — Tools, training, access, or support to maximize my impact

5. My goals for Q2 — Specific, measurable objectives for the next 90 days

Pro Tips for New Data Analysts

1. Document everything in the first month. You'll have "fresh eyes" that see things veterans are blind to. Write it down before you normalize it.

2. Under-promise, over-deliver on timeline. When asked "when can you have this?" add 30% buffer. Delivering early builds trust; delivering late erodes it.

3. Don't critique the existing work in your first month. Even if you see problems, earn trust before suggesting changes. "I noticed X — what's the history there?" is better than "X is broken."

4. Find a buddy — not your manager, but a peer who can answer the "dumb questions" you're embarrassed to ask leadership.

5. Track your wins. Keep a running document of things you delivered and their impact. You'll need this for performance reviews, raises, and resume updates.

Portfolio Project Ideas for Data Analysts

15 project ideas organized by difficulty level. Each includes a description, the skills it demonstrates, and how to make it stand out in a portfolio.

Beginner Projects (0-1 years experience)

1. Customer Segmentation Analysis

What you'll do: Take a public e-commerce or retail dataset, segment customers by behavior (RFM analysis: Recency, Frequency, Monetary), and recommend marketing strategies for each segment.

Skills demonstrated: SQL/Python data manipulation, descriptive statistics, business thinking, visualization, written communication.

How to stand out: Don't just identify segments — recommend specific actions for each and estimate the revenue impact. "High-value at-risk customers (segment 3) represent $X in annual revenue. A retention campaign targeting this group could prevent $Y in churn."

Public datasets to use: Online Retail Dataset (UCI ML Repository), Instacart Market Basket Analysis dataset.

2. Sales Performance Dashboard

What you'll do: Build an interactive dashboard tracking sales KPIs across regions, products, and time periods. Include drill-down capability and highlight anomalies.

Skills demonstrated: BI tool proficiency (Tableau/Power BI/Looker), metric design, visual design, attention to detail.

How to stand out: Add a "so what" page that tells a story about the data — not just raw metrics but actionable insights. Include a written executive summary alongside the dashboard.

3. A/B Test Analysis

What you'll do: Take an A/B test dataset, validate the experimental setup, calculate statistical significance, and make a recommendation.

Skills demonstrated: Statistics, hypothesis testing, sample size calculations, understanding of experimental design, clear communication of uncertainty.

How to stand out: Go beyond just the p-value. Calculate the business impact (revenue, conversion), discuss practical vs. statistical significance, note any risks or caveats, and state a clear recommendation.

Intermediate Projects (1-3 years experience)

4. Churn Prediction and Prevention

What you'll do: Build a model predicting which customers are likely to churn in the next 30 days, then recommend interventions based on the risk factors identified.

Skills demonstrated: Predictive modeling, feature engineering, classification metrics (precision/recall trade-offs), business-centric evaluation, stakeholder communication.

How to stand out: Don't just build a model — simulate the business impact. "If we intervene on the top 100 at-risk customers and retain 20% of them, we save $X in annual revenue. The cost of outreach is $Y, yielding a 5:1 ROI on the retention campaign."

5. Pricing Analysis

What you'll do: Analyze how price changes affect demand using historical transaction data. Model price elasticity for different products or customer segments.

Skills demonstrated: Regression analysis, elasticity estimation, business strategy, revenue optimization thinking.

How to stand out: Provide specific pricing recommendations with expected revenue impact. Show the revenue-maximizing price point and the customer-volume trade-off.

6. Funnel Analysis and Optimization

What you'll do: Map a complete conversion funnel (awareness → interest → trial → purchase), identify the biggest drop-off points, and recommend specific improvements.

Skills demonstrated: Funnel metrics, cohort analysis, identifying bottlenecks, experimental thinking, prioritization.

How to stand out: Quantify the opportunity at each funnel stage. "Improving checkout completion from 45% to 55% would generate 200 additional purchases per month ($30K revenue). Here are three hypotheses for why users drop off at this stage."

7. Market Basket Analysis

What you'll do: Analyze transaction data to find product associations (what's bought together), then recommend cross-sell strategies and product placement.

Skills demonstrated: Association rules, pattern mining, business strategy, translating analytical findings into merchandising actions.

How to stand out: Calculate the revenue uplift potential. "Recommending Product B on Product A's page would show to 5,000 customers/month. At the observed co-purchase rate of 12%, that's 600 additional purchases × $25 = $15K/month."

Advanced Projects (3+ years experience)

8. Causal Impact Analysis

What you'll do: Measure the true causal effect of a business intervention (marketing campaign, feature launch, policy change) using quasi-experimental methods when a true A/B test wasn't run.

Skills demonstrated: Causal inference, difference-in-differences, synthetic control, interrupted time series, understanding of selection bias and confounders.

How to stand out: Explicitly address threats to causal validity. Discuss what could bias the estimate and how you've mitigated it. Show sensitivity analysis under different assumptions.

9. Customer Lifetime Value Modeling

What you'll do: Build a probabilistic LTV model (BG/NBD + Gamma-Gamma or similar) that predicts future customer value based on observed transaction history.

Skills demonstrated: Advanced statistics, probabilistic modeling, business metric design, long-term thinking, model validation.

How to stand out: Validate the model against actual outcomes (hold-out set), segment customers by predicted LTV, and recommend differentiated treatment strategies by segment.

10. Revenue Forecasting System

What you'll do: Build a time-series forecasting system for monthly revenue that accounts for seasonality, trend, and business events. Include confidence intervals and automatic anomaly detection.

Skills demonstrated: Time-series analysis, forecasting methods, uncertainty quantification, system design thinking.

How to stand out: Compare multiple forecasting approaches (ARIMA, exponential smoothing, prophet-like decomposition), explain why you chose one, and show how the forecast improves decision-making (budget planning, hiring, inventory).

11. Multi-Touch Attribution Model

What you'll do: Build an attribution model that distributes credit for conversions across multiple marketing touchpoints, going beyond last-click attribution.

Skills demonstrated: Marketing analytics, Markov chains or Shapley values, business impact analysis, communicating complex methods simply.

How to stand out: Show how the attribution model changes budget allocation recommendations vs. naive last-click. "Under multi-touch attribution, content marketing deserves 3x more credit than last-click suggests — reallocating $50K from paid search to content would yield higher ROI."

12. Experimentation Platform Design

What you'll do: Design (and optionally implement) the statistical framework for an A/B testing program — including sample size calculations, guardrail metrics, early stopping rules, and a decision framework.

Skills demonstrated: Experimental design, statistical power, multiple testing corrections, sequential analysis, system design.

How to stand out: Address real-world complications: What about non-independent observations? How do you handle network effects? What's the minimum detectable effect that's worth testing for given business economics?

Stretch Projects (Demonstrates Senior/Lead Thinking)

13. Metric Definition & Measurement Strategy

What you'll do: For a real or hypothetical product, define the complete measurement strategy: North Star metric, supporting metrics, metric tree decomposition, alert thresholds, and dashboard design.

Skills demonstrated: Strategic thinking, metric design, framework creation, executive communication.

How to stand out: Explain not just what to measure but what NOT to measure and why. Discuss how the metrics change behavior (Goodhart's Law risks) and how you'd detect gaming.

14. Data Quality Framework

What you'll do: Design a comprehensive data quality monitoring system: define dimensions of quality (completeness, accuracy, freshness, consistency), build automated checks, and create an alerting/escalation process.

Skills demonstrated: Data engineering awareness, quality thinking, system design, operational excellence.

How to stand out: Quantify the cost of data quality issues. "In the last quarter, data freshness issues caused 3 incorrect business decisions with an estimated impact of $200K. This framework would have detected those issues within 2 hours instead of 3 days."

15. Analytics Team Operating Model

What you'll do: Design how an analytics team should operate: intake process for requests, prioritization framework, stakeholder communication cadence, documentation standards, and quality assurance process.

Skills demonstrated: Leadership thinking, process design, stakeholder management, scalability thinking.

How to stand out: Base it on real problems. "Analytics teams face [these specific challenges]. This operating model addresses each one through [specific mechanism]. Expected improvement: 30% faster turnaround on requests, 50% reduction in rework."