Essential Skills for Data Engineers in the Age of GenAI

Opportunities and challenges go hand in hand with the explosion of new technologies and tools like Generative AI. There's a notable shift in how enterprises leverage their data. Today, data engineers need to support AI workloads, maintain real-time accuracy, and fine-tune infrastructure within previously unimaginable constraints—just five years ago.

As enterprises scramble to create GenAI-powered products, there is an ultimatum for data teams to evolve. The classic framework of skills is less and less satisfactory, and the boldest visions will be cast by those capable of marrying engineering strength with fluency in AI.

The Evolution of Data Engineering with GenAI

Supplementing knowledge frameworks is a vital pillar that keeps the house of data engineering built around GenAI practices standing tall. Implementing well-structured ETL/ELT processes, which involve extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse, paired with cloud data warehousing, which is the storage of data in a cloud-based environment, constituted the whole of the expectation circle.

GenAI models, on the other hand, require cleaner, more accessible, quicker, and reliably observable data with reasoned quality assurance and explainability.

In simple terms, the role of data engineering has shifted toward enabling strategic decisions.

Why Traditional Data Engineering Approaches Are Dying

Legacy systems do not include recent innovations such as GenAI. They have issues with:

Real-time inference and other activities that require high-speed pipelines.
Non-scaling manual quality audits matching the complexity of the model.
Siloed monitoring tools that have little to no proactive functionality.

These constraints may lead to inaccurate model outputs, exploding cloud expenses, calculated cloud cost, and infrastructure delay. In the age of GenAI, businesses can no longer afford to operate with obsolete methods.

Core Issues for Data Engineers in the GenAI Era

With GenAI’s new application across industries, from real-time personalization to versatile chatbots, data engineers have a new wave of operational problems to tackle. It has become essential (it’s necessary) to build advanced, dependable systems accommodating the speed, scale, and unpredictability of AI workloads.

Let’s focus on the primary problems that need solving:

Efficiently Managing Vast Amounts of Data

GenAI models, and especially large-scale language models, have a datacentric appetite by default. These models consume massive amounts of structured and unstructured data for training and fine-tuning. However, the problem is not simply the increased data volume; rather, how it is managed is what poses a challenge.

Now, data engineers are tasked with the challenge of designing ecosystems that will:

Scale elastically to accommodate sudden surges in volume (think user-generated content, sensor data, or real-time logs).
Support parallel processing for faster training and inference cycles.
Ensure discoverability and access control across growing data lakes.

For example, a GenAI application analyzing customer support transcripts might ingest terabytes of new data daily. Without smart partitioning, metadata tagging, and pipeline optimization, engineers risk creating bottlenecks that slow down model iteration cycles.

Optimizing Cloud Compute Costs

AI workloads don’t come cheap. Training an LLM or supporting a production GenAI system involves heavy usage of compute clusters, GPUs, distributed storage, and high-throughput data processing tools.

The risk? Teams over-provision resources “just to be safe,” only to find themselves burning through budgets without visibility into what’s actually driving the spending.

Data engineers are now expected to:

Monitor resource utilization in real-time.
Eliminate idle compute cycles and redundant pipeline runs.
Right-size infrastructure based on data freshness and business criticality.
Identify waste across multi-cloud environments.

FinOps is no longer a finance-only initiative. It’s baked into the engineering workflow. For instance, deciding whether a model retraining job needs to run every hour or every six hours can drastically impact monthly cloud bills. Similarly, storing cold data in hot storage adds unnecessary costs that could be avoided with tiered storage policies.

Revefi helps engineers get ahead of this by surfacing cost anomalies and offering actionable recommendations before things spiral out of control.

Real-Time Observability

GenAI pipelines are especially delicate since even the smallest of problems can have disastrous results. For example, if a prompt ingestion pipeline begins to skip segments or a new source begins to add biased inputs, the model will be degraded within minutes. This is the reason for real-time observability.

Pipelines are heavily reliant on alert thresholds and manual checks over dashboards. Outdated methods will not work anymore. Engineers require systems that:

Monitoring involves the continuous surveillance of the data’s freshness, volume, and quality within the various sources.
Identify schema drifts and outliers before they disrupt the downstream logic.
Automatically trace the anomalies to the root causes located further upstream.

Consider a GenAI model, for example, that is responsible for creating and producing product descriptions.

If the vendor feed suddenly changes its category label without any prior notice, the model will begin producing goofy or completely incorrect content, which will negatively affect UX and brand image.

Essential Skills for Modern Data Engineers

Adaptation and innovation are crucial in keeping up with the fast pace of GenAI. Data engineers must evolve with the changing landscape, and the skills mentioned above form the foundation of the modern data engineer's toolkit.

1. AI-Driven Data Observability

In the event that currency symbols replace country codes on upstream fields, basic logging will not be sufficient when a GenAI model begins hallucinating. The scenario places logging within the scope of modern observability, which requires plush resources to enable active and predictive interventions, diagnosis, and troubleshooting.

Automated anomaly detection across volume, freshness, and schema.
Column-level lineage to trace errors through complex DAGs.
Predictive insights—knowing where and when your data might break before it impacts downstream ML workflows.

Without this, you’re flying blind. One silent failure in upstream data could propagate misleading outputs across recommendation systems, LLMs, or AI-generated reports. Data observability powered by GenAI itself (like what Revefi enables) is the only way to keep up.

2. Finding Cloud Cost Efficiencies

Managing AI workloads has been and continues to be a capital-intensive activity. Supporting the production of a GenAI system or training an LLM is not cost-effective as it relies heavily on compute clusters, GPUs, extensive storage, and advanced data throughput techniques.

What could go wrong? In an attempt to be conservative, teams might over-provision resources and end up spending all their budget without knowing how or why the money is being spent.

Now, data engineers have these new responsibilities:

Monitor resource utilization in real-time.
Reduce idle compute cycles and teraflops on redundant pipeline executes.
Tier infrastructure to data latency freshness and business value rating.
Cut down on unused resources distributed across differing clouds.

FinOps is no longer a solely financial issue. It is woven into the engineering process. For example, model retraining jobs can be scheduled to run on an hourly basis or every 6 hours; this decision has a monumental impact on costs incurred monthly.

Also, moving cold data to hot storage incurs unnecessary costs that could have been avoided with tiered storage policies.

Revefi improves the situation by preemptively eliminating cost anomalies and giving actionable insights bereft of circular reasoning.

3. Automated Issue Resolution

Observability without automation leads to alert fatigue. And in a GenAI world, where data flows 24/7, manual triage isn’t scalable.

Engineers now need to build self-healing systems—think:

Auto-suspend pipelines when schema changes are detected.
Rollback mechanisms for bad data pushes.
Predefined remediation workflows that fix known data quality issues without human input.

The focus should be on reducing MTTR (Mean Time to Resolution) to minutes, not hours. Platforms like Revefi enable this by not only surfacing issues but also suggesting the why and what next.

4. Scalable Data Infrastructure & Pipeline Optimization

Training GenAI models or feeding them real-time data requires infrastructure that bends without breaking. Data engineers must now:

Design event-driven architectures that adapt to data bursts.
Embrace horizontal scaling patterns to handle dynamic loads.
Ensure fault tolerance so that a failed job doesn’t bring down the whole system.

What used to be an edge case (handling 100M+ rows/day) is now table stakes. And any latency in feeding models can degrade performance. Knowing your orchestration tool (Airflow, Dagster, etc.) is great, but knowing how to optimize each stage of the pipeline is where mastery lies.

5. Automated Data Quality & Governance

The old model of data governance (manual reviews, restrictive access, weekly QA reports) is too slow for AI-driven environments.

Today’s engineers need to:

Bake in validation logic at every transformation layer.
Use machine learning to flag anomalies and drift in model inputs.
Implement policy-as-code governance frameworks that ensure compliance without friction.

Why? Because GenAI systems are only as trustworthy as the data feeding them. If your models make regulatory-impacting decisions (say, in finance or healthcare), poor quality becomes a liability.

6. FinOps & Cost Optimization

In the GenAI era, performance at any cost is a luxury most teams can’t afford. Data engineers must think like FinOps strategists:

Track compute and storage cost per pipeline or job.
Set proactive alerts for budget thresholds.
Pause idle services, switch to spot instances, or downscale non-critical jobs during low demand.

Cost isn’t just an ops concern anymore. When one unnecessary job runs hourly instead of daily, or cold data sits in premium storage, engineers bleed budget—quietly. Tools like Revefi help bridge this gap by marrying observability with cost analytics.

It’s not about just learning new tools. It’s about designing pipelines that can gracefully handle delay, reorder events, and deliver consistent outputs under load—because even a few minutes of stale or out-of-order data can derail GenAI systems in production.

How Revefi Augments Data Engineers’ Ability to Adapt to GenAI

We built Revefi with this very future in mind.

Data engineers using Revefi don’t just get dashboards - they get situational awareness. Through AI-powered observability and advanced cost visibility, Revefi enables teams to:

Detect and fix anomalies at the earliest possible moment across pipelines
Attain real-time visibility into data freshness and reliability as well as its availability
Monitor infrastructure expenditure and resource consumption at a granular level
Maintain a high level of data quality while sustaining rapid development velocity

To summarize, Revefi enables engineers to accomplish more with fewer resources: Less estimation, manual triage, and wasteful spending.

Embrace AI: Choose Revefi

Data engineers now have a new set of responsibilities with the GenAI wave. The traditional approach of moving data from point A to B is obsolete. It is more about making it possible for intelligent systems to learn, adapt, and act at scale.

This transition requires a different approach involving automation, observability, real-time processing, and cost-consciousness.

We are best poised to help data engineers navigate the storm while also providing them with the tools to thrive in this new era.

Explore Revefi today!