A plain-English map of a typical company: what every team actually does, how to decode job titles, and how to get from the role you have to the one you want.
Everything below is free (or has a solid free tier) and runs in a browser with nothing to install. The recommended order is SQL first, because nearly every role that touches data speaks it.
SELECT, WHERE, JOIN, GROUP BY. Learn by typing; all of these run in the browser.
Window functions, CTEs, subqueries, date math. This is the SQL analysts write every day.
Microsoft's BI tool. Learn Power Query (shaping data), data modeling, DAX, and report design.
Drag-and-drop visual analytics. Strong for exploration and polished dashboards.
Python is the lingua franca of data science and data engineering. Focus on pandas for data work.
What separates an analyst from an analytics engineer: version control and dimensional modeling.
A composite of a typical mid-to-large company. Real companies vary (the data org might report to the CIO, CTO, or CFO instead of the CEO; some merge COO and CFO), but the functions exist almost everywhere. Click any box for what that team does, who they work with, and which skills get you there. Use the + buttons to expand a department.
Tap a box to expand it, or search above.
Scroll sideways when expanded. The green badge marks the role you picked above. The small dot on each box is hiring demand: high steady few openings (editorial estimate, Nov 2026).
Job titles are made of two parts: a level (how senior) and a role family (what kind of work). "Senior Data Analyst" = level Senior + family Analyst + domain Data. Once you can read both parts, any title on any org chart becomes legible.
Runs a whole function of the company. Sets strategy, owns the budget, answers to the CEO or the board. "Chief ___ Officer."
Runs several large groups. Found at big companies; often a C-suite member's direct reports.
Owns a major area (VP of Analytics, VP of Engineering). Manages directors. At banks and some firms, "VP" is a much lower mid-level title, so always check context.
Owns a department or large team, usually through managers. Sets priorities and headcount, less hands-on.
First level that manages people. Owns a team's output, hiring, and performance reviews. May still do some hands-on work.
Senior individual contributors who guide others without being their boss. "Lead" sometimes manages a few people; "Principal/Staff" are top-of-ladder experts.
Works independently, owns projects end to end, mentors juniors. Usually 3–6+ years in.
The standard working role. Handles defined work with light oversight. Often "Analyst II" or "Level 2" in numbered systems.
Learning the job. Closely supervised, growing scope. 0–2 years. ("Associate" at consulting and law firms can mean something else entirely.)
Temporary, learning-focused. Often a pipeline into junior roles.
| Blanket term | What it usually means |
|---|---|
| Analyst | Examines data or situations and produces findings and recommendations. Finance, data, business, security, and marketing all have "analysts" doing different work with the same label. |
| Engineer | Builds and maintains systems that have to keep running: software, pipelines, networks, models. Writes code; owns reliability. |
| Developer | Builds software or reports. Often interchangeable with engineer; "BI Developer" and "Power BI Developer" mean building reports and data models. |
| Scientist | Uses statistics and modeling to predict or explain. More research and experimentation than engineering. |
| Architect | Designs how systems fit together before they're built; sets standards. Senior, hands-off-ish, big-picture. |
| Administrator (Admin) | Keeps a specific system healthy: database admin, Salesforce admin, Power BI admin. Configures, secures, supports. |
| Specialist | Deep in one narrow area (payroll specialist, SEO specialist). Usually an individual contributor at mid level. |
| Coordinator | Keeps logistics and schedules moving; often entry-level and administrative. |
| Manager (of a thing) | "Product Manager", "Program Manager", "Account Manager" manage a thing, not people. Only "People Manager" or "Manager, Team X" implies direct reports. |
| Business Partner | A specialist assigned to a department as its advisor (HR Business Partner, Finance Business Partner). Embedded analysts play a similar role. |
| Consultant | Advises rather than owns. Internal consultants or external firm staff brought in for a project. |
| Owner / Lead (of a thing) | "Product Owner", "Data Owner", "Tech Lead": accountable for one asset or workstream's decisions. |
| Operations ("Ops") | Attached to a function, means the people who run its tools and processes: Sales Ops, Marketing Ops, RevOps, DevOps, DataOps, People Ops. |
| Generalist | Covers several areas in a small company; the opposite of a specialist. |
| Head of ___ | Runs a function; level depends on company size (could be a Director or a VP). |
| Chief of Staff | Right hand to an executive: runs planning, meetings, and special projects on their behalf. |
Follow one number, say "monthly active customers", from the system it was born in to the executive slide. Each hand-off is a team on the chart.
CRM, billing, app databases, HR system. Owned by IT's enterprise-apps team and the departments that use them.
Data engineers copy raw data into the warehouse on a schedule.
Analytics engineers clean, join, and model it into tidy tables. Governance defines what "customer" means.
BI developers build shared datasets and metrics (Power BI model, Tableau data source).
Analysts query, explore, build dashboards, answer "why did it move?"
Finance forecasts with it, marketing targets with it, the CEO reports it to the board.
Terms you'll hear in cross-team meetings.
Profit and loss statement. "Owning a P&L" means being responsible for a unit's revenue and costs; a sign of a senior general-management role.
Objectives and Key Results: goals with measurable targets. KPIs are the standing metrics a team watches. Analysts usually build the dashboards for both.
Financial Planning & Analysis: the finance team that builds budgets and forecasts. Heavy Excel users and frequent customers of the data team.
A warehouse (Snowflake, BigQuery) stores modeled tables for SQL. A lake stores raw files cheaply. A lakehouse tries to be both.
Extract, Transform, Load. Modern teams load raw data first, then transform it with SQL inside the warehouse (that's what dbt does).
One place where metrics are defined once so every dashboard agrees. Power BI datasets, Looker's LookML, dbt metrics.
One fact table (events, transactions) surrounded by dimension tables (customer, product, date). The shape Power BI and Tableau love.
Anyone who depends on or influences your work. In analytics, usually the business leader asking the question.
Go-to-market: the combined sales, marketing, and customer-success motion that brings a product to customers.
Service-level agreement: a promised standard ("dashboards refresh by 7am", "tickets answered in 4 hours").
A dataset, dashboard, API, or model treated like software: it has an owner, an SLA, documentation, and users.
Engineering practices for shipping and running models (MLOps) or software (DevOps) reliably: automation, monitoring, rollbacks.