Top 5 High-Paying Hidden Data Roles You Never Knew Existed Beyond Data Scientist

Fraoula
2 days ago
3 min read

Data science often steals the spotlight when it comes to lucrative careers in the data field. Yet, many lesser-known roles offer salaries that match or even exceed those of traditional data scientists. These positions often come with less competition and unique challenges that make them attractive for professionals looking to stand out. Based on 2026 salary benchmarks, some of these hidden roles pay over $150,000 annually, with MLOps engineers earning up to 20% more than standard data scientists. This post explores five such roles that deserve your attention.

Eye-level view of a server rack with glowing lights in a data center — MLOps engineer managing data pipelines

MLOps Engineer

MLOps engineers bridge the gap between machine learning models and production environments. They focus on deploying, monitoring, and maintaining ML systems to ensure they run smoothly at scale. Unlike data scientists who build models, MLOps engineers make sure those models work reliably in real-world applications.

Salary range: $160,000 to $190,000 annually
Why it pays well: Companies rely heavily on automated ML systems, and downtime or errors can be costly. MLOps engineers bring software engineering skills to data science, making their expertise highly valuable.
Key skills: Cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), CI/CD pipelines, Python, and ML frameworks.

This role is growing rapidly as organizations move from experimental ML projects to production-grade systems. If you enjoy both coding and data, MLOps offers a rewarding path.

Privacy Engineer

With data privacy regulations tightening worldwide, privacy engineers have become essential. They design and implement systems that protect sensitive data while enabling its use for analytics and AI.

Salary range: $150,000 to $180,000 annually
Why it pays well: Privacy compliance is critical to avoid legal penalties and maintain customer trust. Privacy engineers combine knowledge of data security, law, and engineering to build compliant systems.
Key skills: Data encryption, anonymization techniques, GDPR and CCPA knowledge, secure software development, and risk assessment.

Privacy engineers work closely with legal teams and data scientists to ensure data is used responsibly. This role suits those interested in ethics and security alongside data.

Close-up of a computer screen showing encrypted data and security codes — Privacy engineer working on data encryption

AI Architect

AI architects design the overall structure of AI systems, integrating various components like data pipelines, ML models, and user interfaces. They ensure these systems meet business goals and scale efficiently.

Salary range: $170,000 to $200,000 annually
Why it pays well: AI architects combine technical expertise with strategic thinking. They guide teams and make high-level decisions that impact the success of AI initiatives.
Key skills: System design, cloud architecture, AI/ML frameworks, project management, and stakeholder communication.

This role is ideal for experienced professionals who want to lead AI projects and shape how AI delivers value in organizations.

Data Engineer

Data engineers build and maintain the infrastructure that allows data to flow smoothly from source to analysis. They create pipelines, manage databases, and optimize data storage.

Salary range: $150,000 to $175,000 annually
Why it pays well: Reliable data infrastructure is the backbone of any data-driven company. Skilled data engineers reduce bottlenecks and improve data quality, directly impacting business decisions.
Key skills: SQL, Python, ETL tools, cloud data warehouses, and big data technologies like Hadoop or Spark.

Though data engineering is well-known, many underestimate its earning potential compared to data science. The demand for these skills continues to rise.

Analytics Translator

Analytics translators act as liaisons between technical teams and business units. They interpret data insights into actionable strategies and ensure projects align with business needs.

Salary range: $150,000 to $165,000 annually
Why it pays well: Companies need professionals who understand both data and business. Analytics translators improve communication, reduce misunderstandings, and speed up decision-making.
Key skills: Data literacy, business acumen, communication, project management, and basic statistics.

This role suits those who enjoy working across teams and translating complex data into clear business value.