There are a lot of roles in data, but the three core fields that most jobs fall under are:
Data Engineering – Building and maintaining data infrastructure.
Data Analytics – Analyzing and interpreting data for insights.
Data Science – Using models to predict and optimize outcomes.
And under each field there are 3 main roles:
Data Engineering → Data Engineer
Data Analytics → Data Analyst
Data Science → Data Scientist
While there are other specialized roles, like Data Architects and AI Engineers, these three are the foundation of most data teams. Below I’ll be diving into each role and going over: what they do; their responsibilities; example tasks; and key skills.
The 3 Big Data Jobs
Each job plays a key role in the data workflow:
Data Engineer - Builds data pipelines and infrastructure
Data Analyst - Analyzes data to provide insights
Data Scientist - Predicts outcomes using machine learning
Data Engineers
What they do: Build and maintain pipelines to collect, process, and store large volumes of data.
Responsibilities
Develop ETL/ELT pipelines to move and transform data.
Ensure data systems are scalable, fast, and reliable.
Work with big data tools like Apache Spark, Kafka, and cloud platforms.
Optimize data warehouses for performance and accessibility.
Example Tasks
Building a real-time data pipeline to ingest and process user activity data from a mobile app.
Automating data ingestion by setting up ETL workflows that pull data from APIs into a data warehouse.
Optimizing cloud storage costs by partitioning and indexing tables in BigQuery.
Key Skills
Programming: Python, Java, Scala.
Big Data & Cloud: Spark, Hadoop, Snowflake, BigQuery, AWS.
ETL & Data Architecture: Designing scalable data pipelines.
Other Jobs in Data Engineering
Data Architect – Designs and oversees data infrastructure at an enterprise level.
ETL Developer – Specializes in building and maintaining ETL/ELT processes.
Platform Engineer – Focuses on developing scalable data platforms and cloud infrastructure.
Data Analysts
What they do: Analyze and interpret data to help businesses make informed decisions.
Responsibilities:
Build dashboards and reports to monitor KPIs.
Perform exploratory data analysis (EDA) to find patterns and trends.
Work closely with stakeholders to translate data into actionable insights.
Use SQL, BI tools (Tableau, Power BI), and Excel for analysis.
Example Tasks:
Creating a customer segmentation report to help the marketing team personalize campaigns.
Investigating a dip in revenue by analyzing sales data and identifying underperforming products.
Building a weekly performance dashboard that tracks key business metrics in Power BI.
Key Skills:
Data Visualization: Tableau, Power BI, Looker.
SQL: PostgreSQL, MySQL
Trend & Pattern Analysis: Spotting key business insights.
Business Acumen: Understanding how data impacts decision-making.
Other Jobs in Data Analytics
Business Analyst – Focuses on business processes and KPIs.
Marketing Analyst – Specializes in customer behavior, campaign performance, and growth metrics.
Financial Analyst – Uses data to track financial performance and forecast trends.
Data Scientist
What they do: Develop models to predict and optimize outcomes using machine learning and statistics.
Responsibilities:
Train machine learning models to detect patterns and make predictions.
Run statistical analysis to understand correlations and causation.
Work on predictive analytics to forecast future trends.
Experiment with algorithms to improve recommendations.
Example Tasks:
Developing a fraud detection model using machine learning to flag suspicious transactions.
Running an A/B test analysis to measure the impact of a new pricing strategy.
Building a recommendation system to personalize product suggestions based on user behavior.
Key Skills:
Programming: Python, R, SQL.
Machine Learning: Regression, classification, clustering, deep learning.
Statistical Analysis: Probability, hypothesis testing, A/B testing.
Other Jobs in Data Science
AI Engineer – Specializes in developing and deploying AI models.
Data Strategist – Focuses on aligning data science with business goals.
MLOps Engineer – Deploys and manages machine learning models in production.
Conclusion
The field of data is constantly evolving (one reason why I love it), and roles often overlap depending on company needs. Whether you’re interested in engineering, analytics, or data science, there’s always room to grow and specialize.