Data Engineering Job Market Overview
Over the past year, data engineering roles have become some of the hottest jobs on the market, both in tech companies as well as businesses outside the IT space. Approximately 150,000 data engineering professionals are employed, with over 20,000 hired last year alone. At a nearly 23% growth rate, data engineering has become a sought-after skill set across North America.
Beyond the workers themselves, data engineering has seen massive startup investments, with over 450 funding rounds recorded in 2024, and globally, the big data market could reach just under $230 billion in 2025 alone.
With states like California, Texas, Washington and Illinois all seeing tight competition for data engineers, job seekers in data are in high demand.
Top Available Data Jobs in North America
Data Engineering Tech Trends
AI and Machine Learning Transforming Data Engineering
AI-driven tools are revolutionizing data engineering by automating key processes such as data integration, transformation, and quality assurance. Machine learning models are eliminating repetitive tasks, allowing engineers to focus on more complex challenges. Additionally, generative AI is playing a growing role in code generation, data pipeline optimization, and schema design, accelerating development cycles and boosting efficiency.
The Shift Toward Real-Time Data Processing
Organizations are increasingly adopting streaming analytics technologies to process and analyze data in real time. This shift is fueling the rise of event-driven architectures, where businesses can respond instantly to data events, enhancing both customer experiences and operational efficiency.
Prioritizing Data Privacy and Security
With evolving data regulations, organizations are investing heavily in compliance-driven data engineering. Privacy-enhancing technologies are becoming standard practices to secure sensitive data while maintaining compliance.
Data Observability and Monitoring on the Rise
Ensuring data quality and reliability is critical in modern pipelines. Data observability tools are providing real-time insights into data health, pipeline monitoring, and downtime reduction. Automated anomaly detection and proactive issue resolution are now essential features of leading monitoring solutions.
Expanding Edge Computing and IoT Integration
With the surge in IoT devices, data engineering is extending beyond traditional cloud environments. Decentralized data processing at the edge is becoming necessary for low-latency applications, reducing bandwidth consumption and improving response times. Edge AI is also gaining momentum, allowing real-time data analysis directly at the source.
Advancing Data Governance and Cataloging
Data governance is becoming more sophisticated with tools focused on data lineage tracking and metadata-driven management. Platforms like Apache Atlas and Collibra are enhancing data discoverability, compliance, and governance, helping organizations maintain high data quality standards.
Sustainability in Data Engineering Gains Traction
As environmental concerns grow, organizations are prioritizing energy-efficient data engineering practices. Optimizing pipelines for lower power consumption and leveraging eco-friendly cloud computing solutions from major providers are key strategies in reducing the carbon footprint of data operations.
Industries Hiring Data Engineering Workers
Cloud Services and AI Infrastructure
As businesses increasingly shift to cloud-based architectures, investments in cloud computing and AI infrastructure are surging. Companies like Amazon Web Services, Microsoft Azure, and Google Cloud Platform are investing billions in expanding their cloud capabilities, creating new opportunities for data engineers.
Enterprises rely on cloud platforms to store and process vast amounts of data, and data engineers design cloud-native solutions that optimize performance, security, and cost-efficiency. AI-driven tools for data analytics, automation, and predictive modeling require robust data pipelines to ingest and process data in real time.
Financial Services
Financial companies are building out robust data infrastructures, integrating artificial intelligence into their core business functions. Because of this, financial institutions have been growing their data engineering teams in order to get the very most out of their newly updated data systems.
Automotive Industry
The automotive sector is undergoing a digital transformation, driven by advancements in electric vehicles, autonomous driving, and connected car technologies. These innovations generate vast amounts of data from vehicle sensors, onboard diagnostics, and driver behavior, requiring sophisticated data engineering solutions.
Automakers also integrate IoT connectivity, enabling real-time vehicle diagnostics and enhancing user experiences through personalized data insights. As automotive companies ramp up their digital and AI capabilities, data engineers play a mission-critical role in ensuring efficient data flow, security, and analytics.
Technology Sector
The technology industry remains one of the largest employers of data engineers, with companies harnessing big data, AI, and cloud computing to drive innovation. The need for professionals who can design, optimize, and maintain scalable data pipelines is more crucial than ever. Tech companies rely on massive datasets for training AI models, personalizing recommendations, and optimizing business operations, requiring data engineers to ensure data is properly collected, processed, and stored for real-time and batch analytics.
From startups to tech giants like Google, Microsoft, and Meta, data engineering is the backbone of digital transformation, making it one of the most sought-after roles in the industry.
Highest Paying Jobs in Data in 2025
These salary averages come from Motion Recruitment's 2025 Tech Salary Guide, based on real market data from thousands of jobs across major North American cities. The figures below represent starting salary ranges for each role, reflecting base compensation only and excluding bonuses, equity, and benefits. Salary variations may depend on factors such as company size, industry, and organizational structure.
Senior Level (5+ Years Experience)
1. Computer Vision Engineer $174,375 – $218,750
2. AI Engineer: $150,769 – $212,692
3. Data Modeler: $159,000 – $202,692
Mid-Level Roles (3-5 Years Experience)
1. Computer Vision Engineer $145,000 – $186,363
2. Data Modeler: $133,076 – $181,346
3. Machine Learning Engineer: $133,076 – $181,346
Download Motion Recruitment's Tech Salary Guide
