Top 5 Alternative Data Career Paths and How to Learn Them for Free

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When you think about a data career, what job titles are the first to spring to your mind? Data analyst, surely. Data scientist? That’s about it. Even a data engineer or a machine learning engineer seems somewhat left-field choice.

If most of you think the same way, no surprise that it’s so hard to land a data scientist job.

Today we will look at some alternative career paths. They might offer you a better chance for employment and even provide you with a more interesting career than those beaten tracks.

 

 

1. Data Product Manager

 
This role is a bridge between business, engineering, and data teams. They define data requirements for data products. This position can be customer-facing or not, all depending on the product(s) you work on.

For example, customer-facing data products would be data APIs, machine learning model interfaces, or client-facing interfaces and dashboards in analytics tools. In your work, you’ll focus on accessibility, scalability, and reliability of the tool; in short, user experience.

In a non-customer-facing role, you could be working on internal dashboards, internal self-serve analytics tools, data pipelines, or machine learning model outputs. The focus here is on dealing with cross-functional needs, getting quick insights, and having reliable data.

To make the picture clear, this is a role where you’ll deal with requirements such as:

• We need a filter by cohort in this dashboard.
• This API needs pagination and access control.
• The churn prediction model needs to be explainable to the customer success team.

Skills Required:

1. SQL & data analytics

• Complex SQL queries
• Exploratory data analysis (EDA)
• Defining key performance indicators (KPIs) and metrics (e.g., DAU/MAU, churn, retention)
• Data validation, i.e., understanding nulls and anomalies in reporting

2. Stakeholder communication

• Writing clear product requirement documents (PRDs)
• Asking the right business questions
• Facilitating (cross-functional) meetings
• Summarizing findings in plain language

3. Product management

• Agile/Kanban/Waterfall/hybrid approaches
• Backlog grooming and writing clear user stories
• Sprint planning and managing tasks in JIRA or similar tools
• Prioritization frameworks (MoSCoW, RICE)
• Understanding technical debt
• Managing delivery timelines

4. Basic UX for dashboards

• Chart type selection
• Dashboard usability principles
• Drill-down vs summary views
• BI tools (e.g., Tableau, Power BI, Looker Studio)

How to Learn for Free:

• How I Would Learn SQL (If I Could Start Over in 2025)
• StrataScratch Analytical Questions
• SQL Exploratory Data Analysis (EDA) Project
• Agile Scrum Full Course In 4 Hours
• Data Visualization Crash Course

 

2. Data Journalist

 
Data journalists tell a story with data using their data analysis and visualization skills. They might be “regular” journalists themselves or analysts who work with journalists to find patterns in public data, verify claims with evidence, and make information digestible through data visualizations.

They might work in newsrooms of the press and electronic media, investigative units (e.g., ProPublica, ICIJ), nonprofit organizations, and think tanks.

The projects data journalists work on might include analyzing government spending records to unveil corruption, creating interactive election visualizations, reporting on climate change, and so on.

Skills Required:

1. Data cleaning

• Excel
• Python (pandas)
• R (tidyverse)
• Filtering, deduplicating, data merging, handling nulls, missing data, and data inconsistencies

2. Data visualization

• Chart types
• Interactive charts
• Chart contextualization (e.g., titles, annotations, footnotes)
• Flourish
• Datawrapper
• Tableau Public
• Observable
• D3.js

3. Storytelling and writing

• Identifying the angle or narrative in a dataset
• Writing headlines and leads that grab attention
• Explaining stats in plain language
• Quoting experts or community members to humanise the story

4. Finding data

• Publicly available datasets (e.g., data.gov, EU Open Data Portal, WHO, World Bank, local government data)
• Scraping websites or requesting data via FOIA

How to Learn for Free:

• StrataScratch Visualization Questions
• DataJournalism.com
• Flourish tutorials
• The Pudding’s open-source process
• Cleaning Data in Excel: Microsoft Excel Crash Course
• Data Cleaning with Python Pandas: Hands-On Tutorial with Real World Data
• Cleaning data in R

 

3. Analytics Engineer

 
Data engineers handle raw data ingestion and storage, while analysts run queries and look for data insights. What are analytics engineers, then? They transform raw data into clean datasets ready for analysis and own analytics layer of a data stack.

Typical tasks for analytics engineers include designing and maintaining a dbt model for data transformation, defining metrics and business logic, and building data marts and semantic layers. They also collaborate with data engineers (upstream) and analysts/product managers (downstream)

In a way, analytics engineers are software engineers of data analytics.

Skills Required:

1. Advanced SQL (for transformation logic)

• CTEs
• Subqueries
• Window functions
• Joins
• Case statements
• Jinja templating
• Building intermediate models
• Data lineage

2. Data Build Tool (dbt) (for analytics engineering)

• Writing models in dbt
• Setting up dependencies and ref() chains
• Building and maintaining model directories (staging -> intermediate -> marts)
• Writing tests (unique, not_null, accepted_values)

3. Git and version control

• Using Git to push/pull code and manage branches
• Commit messages
• Opening pull requests for code review
• Resolving merge conflicts

4. Data warehousing

• Optimizing queries
• Managing datasets, permissions, and schemas
• Incremental models and materializations
• BigQuery
• Snowflake
• Redshift

5. Bonus skills:

• Basic Python – for orchestration or testing
• Looker Studio/Mode/Metabase – for supporting downstream BI tools
• Monte Carlo or Datafold – for data observability

How to Learn for Free:

• How I Would Learn SQL (If I Could Start Over in 2025
• StrataScratch Analytical Questions
• dbt Learn
• Learn Git - The Full Course
• Build a Data Warehouse with BigQuery
• Learn Snowflake in 2 Hours
• Data Warehousing on AWS
• Getting Started with Prefect | Task Orchestration & Data Workflows

 

4. Operations Analyst

 
Operations analysts analyze workflows (e.g., a supply chain, staffing, customer service), identify suboptimal performance, wasted resources and bottlenecks, and propose solutions.

In short, they use data to optimize business operations. Some common examples include delivery optimization, cost reduction analysis, and workforce planning.

In their work, operations analysts will produce reports on KPIs, scenario models to answer questions (e.g, what if the company reduced shifts), dashboards for real-time operation monitoring, and automate tasks.

Skills Required:

1. Excel and SQL

• Building pivot tables and summary reports
• Pulling data from databases
• Cleaning and analyzing data

2. Data visualization tools

• Chart types
• Building dashboards with filters and drill-downs
• Connecting to data sources and automating refresh schedules
• Tableau
• Power BI
• Looker Studio

3. Forecasting and scenario modeling

• What-if models
• Using historical data to forecast trends (e.g., demand curves, seasonality)
• Sensitivity analysis
• Budget impact analysis and capacity planning

4. Process automation

• Zapier/Power Automate/Make – for connecting tools (e.g., send alerts if sales drop)
• Automating report delivery
• Google Apps Scripts – for cleaning up spreadsheets or creating custom workflows

How to Learn for Free:

• How I Would Learn SQL (If I Could Start Over in 2025
• Excel Data Analysis Full Course Tutorial (7+ Hours)
• Tableau Training
• ULTIMATE Power BI Tutorial
• Full Looker Studio Beginners Course
• Python for Data Analysis
• Zapier AI Tutorial for Beginners: Automation Made Simple
• Get Started with Power Automate Desktop: Tutorial for Beginners (2025)
• Make.com Full Course
• Google Apps Script for Beginners

 

5. Data Ethicist/AI Policy Analyst

 
In this job, you will ensure that algorithms and data systems are used in a fair and accountable manner, in line with human rights and social values. The role focuses on the ethical aspects of data-driven technology’s development, deployment, and regulation.

These experts are typically employed by governments, academic institutions, think tanks, and NGOs. You can also find employment in private companies that (are forced to) pay attention to ethics, not only profits.

Typical tasks involve reviewing machine learning models for bias or disparate impact, advising product and legal teams on compliance with data privacy regulations (e.g., GDPR), and contributing to AI policy proposals, model documentation, or audit frameworks. You’ll also work with data scientists to promote explainability and model transparency

Skills Required:

1. Basic understanding of algorithms and model bias

• Supervised vs. unsupervised machine learning
• Model output interpretation (e.g., confidence scores, prediction thresholds)
• Common biases (e.g., sampling bias, label bias, feedback loops)
• Fairness metrics (e.g., demographic parity, equal opportunity)

2. Legal and ethical frameworks

• Data protection laws (e.g., GDPR, CCPA)
• AI-specific legislation (e.g., EU AI Act, FTC guidance)
• Fairness, accountability, transparency, and explainability (FATE) principles
• Applying ethical frameworks like consequentialism, deontology, and rights-based ethics

3. Communication and policy writing

• Writing model documentation and impact assessment
• Translate technical model risks into plain language
• Draft ethics guidelines, policies, or position papers

How to Learn for Free:

• AI Ethics MOOC - University of Helsinki
• EdinburghX: Data Ethics, AI and Responsible Innovation
• Practical Data Ethics
• Data Ethics

 

Conclusion

 
Don’t limit yourself to only few career options if you want to work with data. Not everyone needs to be a data scientist. It’s so hyped up, you’d think it’s the only option. No, it’s not. The five alternatives we mentioned here show how diverse a data career can be. These alternatives allow you to use your technical knowledge with a tangible outcome, and even help make a better society.
 
 

Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.