Feeling confused by job titles in data? You’re not alone.
If you’re an educator or academic exploring the world of data, you’ve probably come across titles like “Data Analyst,” “Analytics Engineer,” and “Data Scientist”, and wondered:
🤔 What do these actually mean?
🧐 Which one is right for me?
🤨 Do I have the skills already, or will I need to start from scratch?
This post is here to clear up the confusion, give you real distinctions between these roles, and show you how your existing skills from teaching or research map beautifully into this world.
🔍 Data Roles at a Glance
Here’s a high-level overview of the most common data roles and what they actually do:
| Role | Focus Area | Typical Tools | Main Outputs |
|---|---|---|---|
| Data Analyst | Interpret data to support decisions | Excel, SQL, BI tools like Power BI, Tableau | Dashboards, reports, insights |
| Data Scientist | Build models to forecast or classify | Python, R | Machine learning models, experiments |
| Data Engineer | Build pipelines and manage data infrastructure | SQL, Python, Airflow, dbt, Spark | Clean, organised, accessible datasets |
| Analytics Engineer | Transform and model data for analysts to use | dbt, SQL, Git, data platforms (Snowflake, Databricks, etc) | Data models, testing frameworks, metrics layers |
| BI Developer | Build and maintain business intelligence systems | Tableau, Power BI, DAX, SQL | Interactive dashboards, self-serve tools |
🧭 These roles can overlap depending on company size. In smaller teams, you might wear multiple hats.
🚀 My Own Journey Into Data
I wanted to be an astronaut growing up. That dictated a lot of my academic background, from Aeronautical Engineering for my undergraduate degree, to a PhD in Materials Engineering funded by the USAF. To be honest, I kind of fell into the world of data by chance!
My first role with data was arguably my postdoctoral research one. When you took away the Materials Engineering context:
- I gathered data from various experiments I conducted;
- I used Python to perform calculations, visualise the trends and make predictions on behaviour; and
- I communicated the outcomes with stakeholders of various levels and technical abilities.
If you look at the table above comparing the various data professions, this looks a lot like a cross between a Data Scientist and a Data Analyst.
Outside of academia, my first data role was as a Data Coach at an EdTech company. This role combined technical instruction in data tools like Python and Excel (which I knew) and SQL (which I had to learn) with business coaching. Although I had not done that before, it essentially involved developing skills and qualities like communicating the value of technical analysis to managers and senior leaders.
I’d always been very good with numbers, thinking logically, and solving problems. These skills were great in academic engineering and turned out to be great in the world of data. If I’m really honest, I think it was more my non-technical skills in communicating at different levels and helping to make the complex simple that helped me to land my first role outside of academia. These were skills I primarily developed through teaching and lecturing.
I do find it hard to identify with a single data profession or title. I just think of myself as a data professional, willing to learn whatever tool is needed and do what’s necessary to get the job done. With my postdoc role, I was more of a Data Scientist. In my first coaching role, I was more of an Analyst. Currently, I work closely with Data Engineers and Analytics Engineers, which makes it easier to identify with them.
🧠 Transferable Skills from Teaching & Research
The good news? You’re likely already doing many “data” things — you’ve just never called them that.
Here’s how common educator and academic skills translate into the data world:
| Education or Research Skill | Mapped Data Skill | Applies To |
|---|---|---|
| Designing lesson plans or curriculum | Structuring analysis, building data pipelines | Analytics Engineer, Data Analyst |
| Grading and assessment analysis | Descriptive statistics, KPI reporting | Data Analyst, BI Developer |
| Academic research, lit reviews | Hypothesis testing, exploratory data analysis | Data Scientist, Data Analyst |
| Communicating with students and other stakeholders | Storytelling with data, stakeholder alignment | All roles |
| Creating rubrics and standards | Defining metrics, building data models | Analytics Engineer, BI Developer |
| Working with messy student data | Data wrangling, cleaning | Data Analyst, Data Engineer |
| Supervising research projects | Managing data projects, experimentation | Data Scientist, Analytics Engineer |
| Writing reports and papers | Writing dashboards, documentation | BI Developer, Data Analyst |
🧩 So… Which Role is Right for You?
Here’s a quick way to self-assess:
| If you enjoy… | Explore roles like… |
|---|---|
| Telling stories with data | Data Analyst, BI Developer |
| Building tools for others to use | Analytics Engineer, BI Developer |
| Organizing and cleaning messy data | Data Engineer, Data Analyst |
| Running experiments and testing hypotheses | Data Scientist, Data Analyst |
| Thinking about systems and how they connect | Analytics Engineer, Data Engineer |
🛠️ What to Learn First (Based on Role Interest)
Here’s a suggested starting point if you’re new:
- All roles: SQL, Excel, communication
- Data Analyst: BI tool like Power BI/Tableau, commercial awareness, storytelling
- Analytics Engineer: dbt, Git, data modeling, testing
- Data Scientist: Python, statistics, ML frameworks/models
- Data Engineer: Python, ETL, cloud platforms, orchestration tools
💭 Final Thoughts
You don’t have to pick the perfect role before you start.
In fact, many people move between these roles once they’re in the field. What matters most is starting where your strengths already shine and letting your learning and career evolve from there.
You already have many of the right skills. Now it’s just about learning how to talk about them in a data context and getting experience with a few tools under your belt. I will be writing about how to do that in an upcoming post, so sign up for my mailing list.
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