How Generative AI (GenAI) is Transforming Data Engineering

Data engineers have long been the backbone of modern businesses, building and maintaining the pipelines that power data-driven decision-making. With the rise of generative AI (GenAI), this role is evolving rapidly. Traditional automation has helped streamline repetitive tasks, but GenAI is fundamentally reshaping how data is processed, optimized, and governed.

According to a survey by HSBC, 61% of senior business and technology decision-makers see generative AI as a game-changer in their business. Since the last 3 years, we’re witnessing a paradigm shift in how enterprises manage, transform, and extract insights from their data with the help of GenAI.

In this article, we will explore the differences between GenAI and traditional automation, examine the evolving responsibilities of data engineers, and discuss how AI is revolutionizing data transformation, quality, optimization, and governance. Finally, we’ll explore the challenges data engineers face in adopting this new wave of AI-powered solutions, and how Revefi steps up to handle your data engineering needs.

GenAI vs. Traditional Automation

Automation has been a staple in data engineering for decades. Traditional automation relies on rule-based scripting, predefined workflows, and deterministic models to process and analyze data. While effective, these methods struggle with unstructured data, unexpected anomalies, and dynamic business requirements.

GenAI, on the other hand, operates on advanced machine learning models, such as Generative Adversarial Networks (GANs) and Large Language Models (LLMs), which can generate, augment, and interpret data. It goes beyond static automation by:

1. Handling unstructured data

Most business data, like social media posts, images, and videos, is unstructured and difficult to analyze. GenAI extracts meaningful insights from this chaos, enabling businesses to track sentiment, detect trends, and enhance decision-making with previously untapped information.

2. Enhancing data quality

Incomplete datasets hinder accurate analysis and predictions. GenAI can generate realistic synthetic data to fill gaps, reducing errors and improving model reliability. This ensures data-driven decisions aren’t compromised by missing or inconsistent information.

3. Enabling adaptive learning

Traditional models become outdated as data patterns evolve. GenAI continuously refines models by learning from new trends, making AI-driven insights more relevant, adaptive, and capable of responding to dynamic business needs in real-time.

"Traditional automation is about efficiency; GenAI is about intelligence," says Holly Robertson, Vice President of Quantitative Investment Solutions Sales at HSBC Global Markets. "It helps us synthesize both traditional and alternative data to uncover deeper insights."

Traditional Data Engineering Landscape

Before delving into it, let us first understand the core responsibilities of data engineers in a traditional setup. Historically, data engineers have focused on:

1. Data ingestion and integration

Businesses collect data from multiple sources, which include databases, APIs, and external files. Data engineers traditionally built ETL pipelines to put this data into warehouses. GenAI can automate this integration, reducing manual effort.

2. ETL (Extract, Transform, Load) processes

Raw data is messy, inconsistent, and unstructured. Data engineers spent hours cleaning and standardizing it for analytics. GenAI can accelerate this process by automating transformations and applying intelligent fixes at scale.

3. Data pipeline management

Data flow between sources and destinations is critical to prevent bottlenecks. Previously, engineers manually monitored and optimized pipelines. AI tools now detect anomalies, automate troubleshooting, minimizing downtime.

4. Performance optimization

Query speed and storage efficiency directly impact decision-making. Engineers traditionally fine-tuned indexing, caching, and partitioning for better performance. GenAI automates optimization, dynamically adjusting configurations based on usage patterns to enhance efficiency.

5. Data governance and security

Regulations like GDPR and CCPA mandate strict data privacy and compliance. Engineers have relied on rule-based approaches to enforce security policies. GenAI enhances governance by automating compliance checks, flagging risks, and generating synthetic data for privacy-preserving analytics.

While these tasks remain crucial, the advent of GenAI is shifting how they are executed.

How AI is Transforming Data Engineering Landscape

What are the various ways companies are using AI to get the most out of their data engineers? While GenAI can significantly boost performance and allow engineers to ship code faster than ever before, there are a multitude of other benefits that engineers have adopted to transform data with AI.

1. Data Transformation

AI-powered tools can automatically map, clean, and enrich datasets without human intervention. LLMs like GPT-4 can understand and interpret data relationships, reducing manual effort in schema generation and feature engineering.

2. Data Quality & Augmentation

Incomplete datasets have long been a problem for data engineers. GenAI, using synthetic data generation, can fill in missing values while maintaining statistical integrity. IBM reports that 90% of the world’s data was created in the past two years, highlighting the growing need for AI-powered data cleansing solutions.

3. Optimization & Performance

AI-driven data indexing and query optimization improve system efficiency, cutting down processing times by up to 40%. Automated anomaly detection reduces the need for manual debugging and data validation.

4. Governance & Compliance

AI-driven tools assist in monitoring compliance frameworks, reducing the risk of violations. Synthetic data can be used for testing and analytics while preserving privacy, an essential factor given stringent regulations like GDPR.

How GenAI Can Help Data Engineers

GenAI shouldn’t be seen as a way to replace data engineers. It’s here to make them more efficient and valuable. Some key ways it enhances their workflow include -

1. Automating tedious tasks

Data engineers spend a significant amount of time writing and maintaining scripts for ETL processes, data cleaning, and schema management. AI automates these repetitive tasks, reducing manual effort and minimizing errors.

2. Generating data visualizations

Traditional reporting requires manually querying databases and formatting data for dashboards. AI-powered tools now generate real-time visual analytics, automatically identifying trends and anomalies. Engineers can quickly build interactive reports without coding with GenAI.

3. Improving predictive analytics

AI enhances forecasting by analyzing historical patterns and detecting correlations that humans might miss. This enables businesses to anticipate demand fluctuations, detect fraud, and optimize supply chains. With AI-driven insights, data engineers can build more robust and adaptive models for real-time decision-making.

4. Enhancing data security

Data breaches and compliance violations are major concerns for enterprises. AI-native solutions like Revefi's Raden, continuously scan for suspicious activities, anomalies in data access, and cost inefficiencies. By providing real-time alerts and automated remediations, AI helps data teams proactively manage security risks and governance challenges.

Challenges of Using GenAI in Data Engineering

Despite its promise, the integration of GenAI into data engineering isn’t without challenges:

1. Model Transparency & Explainability

AI models function as black boxes, making it difficult for engineers to understand how decisions are made. This lack of transparency can lead to mistrust, especially in high-stakes industries like finance and healthcare. Solutions like explainable AI (XAI) are being explored to improve interpretability, helping engineers validate outputs and meet regulatory requirements.

2. Bias & Ethical Concerns

AI models trained on biased data can produce skewed results, affecting decision-making. This can lead to unfair treatment in hiring, lending, or medical diagnoses if not properly addressed. Engineers must ensure diverse, unbiased datasets for fair outcomes, using bias-detection tools and responsible AI frameworks to mitigate ethical risks.

3. Skill Gaps

Traditional data engineers may lack expertise in AI/ML, requiring upskilling in areas like prompt engineering and LLM fine-tuning. Organizations must invest in continuous learning programs to bridge this gap, ensuring teams can leverage AI-driven automation without compromising data quality and governance.

4. Data Privacy & Security Risks

The use of AI-generated synthetic data raises concerns about regulatory compliance and data security. Unauthorized access or poorly managed synthetic datasets can expose sensitive information. Companies must implement robust governance frameworks to mitigate risks, ensuring AI solutions align with regulations like GDPR and HIPAA.

The Human + AI Collaboration with Revefi’s Raden

Revefi's Raden is an AI-powered autopilot and copilot designed to enhance data engineering teams' efficiency and effectiveness. By automating tasks such as data observability, quality assurance, performance monitoring, and cost management, Raden allows engineers to focus on strategic initiatives.

Raden’s zero-touch setup delivers results in just five minutes, leading to a 50% reduction in data spend and a tenfold increase in operational efficiency. Raden's integration of generative AI and machine learning enables continuous optimization of data architectures and system performance, ensuring that data operations are both cost-effective and high-performing.

Curious how Revefi’s Raden can help your business? Book a free call here!

Conclusion

Generative AI is changing data engineering, moving beyond traditional automation to provide deeper insights, enhance efficiency, and improve data quality. While challenges remain, forward-thinking data engineers who look forward to using AI will play a major role in shaping the future of decision-making.

As we move toward a future where AI-generated data may constitute over 60% of training sets, the demand for skilled data engineers will only grow. Those who adapt and harness the power of GenAI will not only stay relevant but become indispensable.