Focuses on the development, construction, maintenance, and testing of data architectures, such as databases and large-scale processing systems.

Skills: SQL, Python, ETL processes, big data technologies (e.g., Hadoop, Spark), cloud platforms.

Gathers, processes, and analyzes data to generate insights that guide business decisions. They typically use statistical tools and data visualization software.

Skills: Excel, SQL, Python/R, data visualization tools (e.g., Tableau, Power BI).

Develops and manages BI solutions, including dashboards and reporting tools, to help organizations make data-driven decisions.

Skills: SQL, data warehousing, BI tools (e.g., Tableau, Power BI), ETL processes.

Builds and deploys machine learning models. Although this role is often linked to AI, it's heavily data-centric, involving data preprocessing, model training, and evaluation.

Skills: Python, TensorFlow, PyTorch, data processing, software engineering.

Specialices in working with large-scale data processing frameworks and big data technologies to handle and analyze massive datasets.

Skills: Hadoop, Spark, NoSQL databases, Python/Scala, data warehousing.

Focuses on creating visual representations of data to make complex information accessible and understandable. They work closely with analysts and decision-makers.

Skills: Data visualization tools (e.g., Tableau, Power BI), graphic design, data analysis, communication.

Designs and manages the data infrastructure and architecture within an organization, ensuring data is effectively collected, stored, and utilized.

Skills: Database management, cloud platforms, SQL, data modeling, big data technologies.

Manages data products, including their development and lifecycle. They work at the intersection of data science, engineering, and business strategy.

Skills: Product management, data analysis, SQL, communication, project management.

Ensures the accuracy, completeness, and reliability of data. They often work on identifying data anomalies and implementing data quality improvements.

Skills: SQL, data analysis, attention to detail, data validation tools.

Implements processes, tools, and methodologies to ensure data flows smoothly from source to destination, optimizing the data lifecycle.

Skills: Data pipeline management, automation, cloud platforms, SQL, scripting languages (e.g., Python, Bash).

Focuses on establishing policies and procedures for managing data assets within an organization to ensure data quality, security, and compliance.

Skills: Data management, compliance (e.g., GDPR), data quality, project management, communication.

Applies mathematical and statistical methods to financial and risk management problems, often in the finance and investment industries.

Skills: Advanced mathematics, statistics, programming (Python/R), financial modeling.

An executive role focused on overseeing a company's data strategy, including data governance, data quality, and the overall data-driven culture of the organization.

Skills: Leadership, data management, business strategy, communication, governance.