Big Data Engineers and AI Tools: What You Need to Know | Dice.com Career Advice

Posted on Author AIJobNexus

This post was originally published on this site.

A data engineer plays a critical role in designing, building, and maintaining systems to process and manage large-scale datasets, enabling businesses to turn raw data into actionable insights. How is that role changing in this era of rapidly evolving AI tools?

This involves creating scalable, reliable, and efficient data pipelines that handle diverse data formats and support modern analytics and decision-making processes. With the rise of data lakes and lakehouses, data engineers are at the forefront of managing these complex systems to maximize the value of an organization’s data assets.

Abby Kearns, CTO of Alembic, says that, in addition to building the infrastructure, data engineers are responsible for ensuring high system performance, reliability, and accessibility.

“They leverage modern tools to automate workflows, manage metadata, monitor pipelines, and enforce data quality, governance, and security,” she explains. “Their work provides the foundation for analytics, machine learning, and AI-driven initiatives.”

Data engineers rely on a diverse ecosystem of tools to handle every stage of the data lifecycle. These include tools for:

How AI Can Help

Data engineers are increasingly leveraging AI-powered tools to enhance efficiency, improve data quality, and streamline processes across the data lifecycle.

Tools like Fivetran and Airbye use AI to automate data integration and pipeline creation, while platforms like Monte Carlo and Databand rely on AI to monitor data pipelines, detect anomalies, and ensure data reliability.

AI also plays a critical role in metadata management, with tools like DataHub and Amundsen offering intelligent data discovery and lineage tracking, making it easier to understand and manage large-scale datasets.

Similarly, AI-driven workflow orchestration tools such as Airflow and Prefect provide predictive insights and automated retries to optimize pipeline execution.

AI tools can significantly enhance the productivity of data engineers and Big Data engineers by acting as intelligent assistants that optimize workflows. “A good data engineer is someone who is open to suggestions, and AI can offer valuable insights based on past activities,” says James Stanger, chief technology evangelist at CompTIA.

For example, AI tools can identify repetitive tasks or inefficiencies in a workflow and propose improvements. “It might suggest, ‘You’ve been doing this task repeatedly—why not streamline it this way?’” Stanger explains.

He compares the process to personalized recommendations on platforms like Amazon, where users are prompted with suggestions based on prior actions. “These tools don’t just guide step-by-step processes but help engineers create more efficient systems overall, making AI an essential collaborator in data-driven environments,” Stanger says.

Kearns adds that, beyond integration and orchestration, AI tools are enhancing query performance and enabling real-time data processing. Platforms like Snowflake and Trino use AI to optimize query execution, while Apache Kafka integrates machine learning models for real-time predictions.

Monitoring tools like Bigeye and Anodot leverage AI for proactive anomaly detection and data freshness validation.

“Even in the development phase, AI-assisted coding tools such as GitHub Copilot and Cursive help engineers write efficient pipeline code and transformations,” she says.

By automating repetitive tasks, optimizing performance, and improving data reliability, these AI-powered tools allow data engineers to focus on building smarter, more robust data systems.

Essential Understanding

While AI brings immense value to big data engineering, it has its limitations that engineers must address.

One major challenge is the dependency on high-quality training data—AI models trained on incomplete or biased datasets can produce inaccurate or unreliable results. “AI often lacks contextual understanding, making it prone to misinterpreting data anomalies or trends without domain-specific insights,” Kearns notes.

Scalability can also be an issue, as processing extremely large datasets requires substantial computational resources, which can increase costs and complicate resource management.

Moreover, many AI tools rely on predefined models that may not align with specific business needs, and customizing these models can require significant expertise and effort. “AI is still a very naïve technology,” Stanger notes, emphasizing the need for robust training.

Just as a new employee requires time to grasp their work environment and provide meaningful input, AI also needs to be taught the nuances of its operating context. It can take months for a skilled employee to fully integrate into their environment, and AI faces a similar learning curve.

The challenge lies in ensuring AI comprehends its surroundings and context deeply enough to make valuable suggestions, improve workflows, and avoid inefficiencies.

“Training remains a crucial yet often underestimated aspect of deploying AI effectively,” Stanger says.

AI Training: Where to Go

Kearns says data engineers have access to numerous training resources to build and enhance their skills.

Online learning platforms include Coursera, Udemy, and edX, which offer courses and certifications on tools and technologies like Apache Spark, Airflow, and cloud platforms such as AWS, Google Cloud, and Azure.

Some key certifications include:

+ AWS Certified Data Analytics

+ Google Cloud Professional Data Engineer

+ Databricks Certified Data Engineer Associate

“Additionally, hands-on platforms like DataCamp and Kaggle allow engineers to practice building pipelines and solving real-world problems using public datasets, often supported by free-tier credits from major cloud providers,” she says.

For in-depth learning, engineers can explore open-source documentation for projects like Apache Spark, Kafka, and Airflow, or dive into books like “Fundamentals of Data Engineering” by Joe Reis and “Designing Data-Intensive Applications” by Martin Kleppmann.

Community-driven platforms like GitHub, newsletters such as “Data Engineering Weekly,” and podcasts like “Data Engineering Podcast” offer insights into the latest industry trends.

“For a more guided approach, mentorship programs, bootcamps, and contributing to open-source projects provide practical experience and networking opportunities,” she says.

By blending formal training, self-learning, and active community participation, data engineers can stay current in a rapidly evolving field.

Securing Executive Buy-In for Upskilling

To secure executive buy-in for AI training resources, it’s crucial to frame data literacy as a foundational need, not a luxury, says Stanger: “Ten years ago, having data was a nice-to-have; now, it’s a must-have.”

Many organizations have failed to align workforce training with the explosion of data and information technology. “We haven’t provided enough data literacy, big data literacy, or information literacy to take full advantage of what’s now available,” Stanger adds.

Leaders must understand that investing in training is essential for staying competitive. From Stanger’s perspective, building a workforce skilled in curating and leveraging data is no longer optional—it’s critical for long-term organizational survival.

Kearns says investing in AI training for data engineers offers clear, measurable benefits that align with business goals, and notes AI training mitigates risks by improving observability and governance in data systems.

“AI-based monitoring tools can proactively identify and resolve pipeline failures, minimizing the impact of data issues on operations,” she says. “Compliance and security tasks can also be streamlined with AI, reducing the risk of regulatory penalties.”

By showcasing real-world success stories and tying AI training to tangible outcomes—such as cost reduction, faster insights, and increased revenue—data engineers can make a compelling case for this investment.

“Ultimately, training in AI technologies positions organizations to scale efficiently, innovate faster, and thrive in an increasingly data-driven landscape,” Kearns says.