Three Major Tech Trends Reshaping Data Engineering

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Coming toward the end of the year, 2025 promises to be more challenging as the compounding investments in AI have a direct effect on the field of Data Engineering. Accel Venture Capital estimates that investment in AI across the US, Europe & Israel this year will be just shy of US$80 billion - that’s up 27% on last year. This means there have been an incredible lift in the number of hires for Data & Ai Engineering in 2024.

While Data, Business Intelligence & Analytics hires are up 50% on 2023, it is difficult to ignore the staggering 285% increase in hires for AI Engineering. Both professions have the potential to be the most consequential as we enter the second quarter of the 21st Century.

As you begin to look forward to the new year, we thought it a worthwhile to see what the analyst firm Gartner had to say about the Top Strategic Technology Trends are for 2025. This article sets out to understand how these trends will impact your career as a Data Professional over the next few years. so buckle-up as we take a dive into the essential technologies you need to be paying attention to, as we head into the Holiday Season.

1. AI Imperatives and Risks

The first category centres around AI, as the colossal investment in Neural Networks, Language Models and AI agents takes off.

🕵🏼‍♀️ Agentic AI: The Rise of Autonomous Decision-Making

We are already seeing examples of Agentic AI in workflows, and this trend is set to explode in 2025. As the models become ever more capable, purpose oriented agents are being deployed to make independent decisions, and take actions to achieve specific goals. The role of engineers is gradually shifting from coding, to training, and overseeing the accuracy of the answers being generated by agents built for specific activities. For data engineers, this trend presents both challenges and opportunities. On the challenge side of the ledger we are set to see:

• Enhanced Data Pipeline Automation: Agentic AI will likely revolutionise data pipeline management, automating complex decision-making processes for data ingestion, transformation, and storage.

• Skill Adaptation: Data engineers will need to develop skills in designing and implementing AI-driven systems that can autonomously manage and optimise data workflows.

• Ethical Considerations: As AI becomes more autonomous, data engineers must be well-versed in AI ethics and responsible AI practices to ensure these systems operate within acceptable, and newly regulated boundaries.

And on the opportunity side, new roles and responsibilities are likely to emerge:

• AI Pipeline Architect: Designing data pipelines that integrate seamlessly with agentic AI systems.

• AI Ethics Specialist: Ensuring AI-driven data systems adhere to ethical guidelines and regulatory requirements.

• Autonomous Data System Manager: Overseeing the operation of self-managing data ecosystems powered by agentic AI.

🙅🏽‍♂️ AI Governance Platforms: Ensuring Responsible AI Use

As AI becomes more prevalent, the need for robust governance frameworks will be necessary, not only to comply with regulation, but also to protect the value of your Enterprise’s brand. A number of governance platforms are emerging as critical tools for managing and controlling an array AI systems, ensuring they are used legally, responsibly, and ethically. On your To Do list for 2025 are:

• Compliance Integration: Data engineers will need to incorporate AI governance principles into their data architectures and pipelines.

• Transparency and Explainability: Designing data systems that support transparent AI decision-making processes will become crucial.

• Monitoring and Auditing: Implementing robust monitoring and auditing mechanisms for AI-driven data systems will be a key responsibility.

Emerging roles and capability sets that we predict will be become valuable in 2025:

• AI Governance Specialist: Developing and implementing AI governance frameworks within data ecosystems.

• Explainable AI Engineer: Creating data models and systems that provide clear insights into AI decision-making processes.

• AI Compliance Auditor: Ensuring data systems and AI models adhere to regulatory standards and ethical guidelines.

🗞️Disinformation Security: Safeguarding Data Integrity

The rise of digital misinformation and so-called Fake News, disinformation security has become a critical concern. Data professionals need to be vigilant as to sources and the potential for those sources to be corrupted - not just by humans, but by Language Models as they are deployed in decision making workflows. Data Quality, Integrity and Authenticity will become even more important in 2025. As the use of data in digital systems becomes more complex some additional challenges include:

• Data Verification Systems: Developing robust systems for verifying the authenticity and origin of data will be crucial.

• Anomaly Detection: Implementing advanced anomaly detection mechanisms to identify potential disinformation in data streams.

• Blockchain Integration: Exploring blockchain technologies for creating tamper-proof data records and audit trails.

Where some opportunities lie as you think about your career going into 2025:

• Data Integrity Specialist: Focusing on maintaining the accuracy and trustworthiness of data throughout its lifecycle.

• Disinformation Detection Engineer: Developing algorithms and systems to identify, and flag, potential disinformation in data sets.

• Blockchain Data Architect: Designing data systems that leverage blockchain for enhanced security and traceability.

2. New Frontiers of Computing

From the four trends in this category, we believe the most significant two are:

🤔 Post-Quantum Cryptography: Securing Data for the Quantum Era

As quantum computing advances, the need for post-quantum cryptography (PQC) becomes increasingly urgent. This trend will have significant implications for data security and encryption practices, including the need for Cryptographic Agility. This means developing data systems that can quickly adapt to new cryptographic standards. Along with the use of Quantum-Resistant Algorithms in data storage and transmission systems.

Amongst the challenges here will be the migration of legacy systems to quantum-resistant architectures.

Enterprises will be looking for professionals with expertise across Quantum-Safe Data Architecture, Post-Quantum Cryptography Specialists and Quantum-Data-Ecosystem Risk Analysts.

💡Ambient Invisible Intelligence: The Ubiquity of Data Collection

The proliferation of low-cost tags, sensors and devices will lead to an explosion in data collection from everyday objects that surround us. This trend will significantly impact how data is gathered, processed, and ultimately utilised. This trend will mean Data Professionals will need to be thinking about:

• Edge Computing: Developing expertise in edge computing architectures to process data closer to its source.

• Real-Time Data Processing: Implementing systems capable of handling and analyzing vast amounts of real-time data from diverse sources.

• Data Privacy and Security: Addressing the privacy and security challenges associated with ubiquitous data collection.

This means there will be new opportunities for:

• IoT Data Architects: Designing data systems that can efficiently handle data from numerous IoT devices.

• Edge Computing Specialists: Focusing on developing and managing edge computing solutions for real-time data processing.

Other trends of note in this category are Energy-Efficient Computing or Sustainable Data Management, and Hybrid Computing that leverages Diverse Computational Resources, including CPUs, GPUs, edge devices, and quantum systems. Each will create new opportunities and challenges in data processing and analysis.

Interested in the report from Gartner?

You can find it here.

3. Human-Machine Synergy

The lines between the natural and artificial worlds have been blurring for years now and 2025 will only accelerate this trend as Augmented Reality bites both in what we perceive and how use robotics. Data Engineers have a big role to play in this brave new world across:

🥽 Spatial Computing: Bridging the Physical and Digital Worlds

Spatial computing, which augments reality with digital content, will create new paradigms for data interaction, visualisation and autonomous decision making. Challenges include:

• 3D Data Modeling: Developing skills in 3D data modeling and spatial data management.

• Real-Time Data Integration: Implementing systems that can seamlessly integrate real-time data into spatial computing environments.

• Augmented Analytics: Creating data visualization and analysis tools that leverage spatial computing for enhanced insights.

New specialisations will emerge for Spatial Data Engineers, AR/VR Data Visualisation Experts and specialist Spatial Analytics Architects. All of whom will play a part in leverag spatial data for advanced analytics and decision-making.

🤖 Polyfunctional Robots: Data-Driven Automation

The rise of truly versatile robots capable of performing multiple tasks are already creating new demands for data-driven automation and control systems.

This is opening up the fields of:

• Robotic Process Automation (RPA): Expanding expertise in RPA to support the integration of polyfunctional robots into data workflows.

• Sensor Data Processing: Developing systems to efficiently process and analyze data from robotic sensors.

• Machine Learning for Robotics: Implementing machine learning models to enhance robot decision-making and adaptability.

As you contemplate your next career move could it be as a Robotics Data Engineer, or Sensor Fusion Specialist - focusing on integrating and analysing data from robotic sensors, or as a AI-Driven Robotics Programmer - developing AI models and algorithms for enhancing robot functionality and adaptability?

🤯 Neurological Enhancement: The Frontier of Human-Data Interaction

Neurological enhancement technologies are opening up new paradigms for human-data interaction presenting challenges and new roles around:

• Brain-Computer Interfaces (BCIs): Developing expertise in processing and interpreting data from BCIs.

• Neurodata Management: Creating systems for securely storing and managing highly sensitive neurodata.

• Cognitive Computing Integration: Implementing systems that can interface between human cognition and data systems.

The most Consequential Field to be in as we look forward

With the first quarter of the 21st Century behind us, it's clear that the field of Data and AI Engineering is one of the most consequential in the digital age. Your career prospects in a time where data is more ubiquitous, and is integrated into every aspect of business and society, are enormous. To thrive in this rapidly evolving landscape, data professionals we would suggest you consider the importance of investing in:

• Continuous learning - especially in the application of AI into your own workflows, not just that of the business

• Cultivating Interdisciplinary Skills - in adjacent fields such as robotics, spatial computing, and energy efficiency.

• Paying attention to Ethical Considerations - where you develop a strong understanding of data ethics and governance to ensure responsible use and minimise the potential harm of AI, Robotics and Cyber Physical systems.

Over the coming weeks we will be doing a deep dive into each of the most significant aspects of these trends to ready you for the big challenges of 2025.

Last up today, is a highlight from the Data Innovators Exchange, launched recently is the Enterprise AI Engineering Classroom and Resource Hub. You will find materials to get you started using IBM Watsonx in an enterprise environment.

Like this content? Join the conversation at the Data Innovators Exchange.

That’s a wrap for this week.

Thank you