Common Data Engineering mistakes and how to avoid them
From broken pipelines to unexpected cloud costs, learn from real-world mistakes and lessons to level up your data engineering skills.
Growth often starts with a mistake. It could be yours, something a teammate did, or even a story you read on Reddit. Either way, mistakes are powerful teachers, especially in data engineering, where one small slip can ripple into broken pipelines, massive cloud bills, or worse, frustrated end users.
In this post, we want to share a mix of lessons, some of which we’ve learned the hard way, some gathered from the Data community itself, and others from the experts in the field. While the examples focus on data engineering, the insights can help anyone working with data.
We’ve categorised these lessons into the following areas:
Technical Infrastructure
Process and Methodology
Security & Compliance
Data Quality & Governance
Communication
Career Development & Growth
Finally, we’ll conclude by sharing Best Practices for Junior Data Engineers to help guide their growth in the field.
Before we begin, we'd love to hear about a mistake you've made in your data career and the lessons you've learned from it. Share your experience with us and the community so we can all learn and grow together 🙂.
Technical Infrastructure Mistakes
Fragile Data Pipelines
One common issue in data engineering is skipping proper CI/CD pipelines and integration testing. It often starts with a quick release or a tight deadline, teams push code directly without automated validation. At first, everything seems fine. But over time, unexpected changes, broken dependencies, or mismatched data types creep in.
The result? Pipelines fail without warning. Data either doesn’t get updated or arrives in an unusable format.
To avoid this, build testing into your CI/CD pipeline. Automated schema checks, integration tests, and pipeline validation can catch these issues before they affect production.
Exception Handling Failures
When data pipelines run without proper error handling, failures often go unnoticed. A broken API response, a null value, or a permission issue might cause a job to stop (or even worse, pass silently with incomplete data). This creates a risk of working with inaccurate or partial datasets, which is especially dangerous in reporting.
Without alerts or logs, engineers may only find out when stakeholders question the results.
Robust pipelines should include clear exception handling: retries, failure notifications, logging, and recovery paths. Alerts (email, Slack, etc.) ensure issues are caught and resolved quickly before they cause downstream problems.
Costly Cloud
Cloud platforms offer flexibility, but poor management can quickly become expensive. Forgotten clusters, inefficient queries, or over-provisioned compute can lead to surprise bills that some teams have accidentally racked up thousands in just days.
Examples include:
Leaving a Redshift test cluster running for a year.
Writing billions of rows daily into PostgreSQL, causing replication lag.
Running
SELECT DISTINCT *on huge BigQuery datasets without filtering.Ignoring table partitioning, leading to full scans and high query costs.
These issues not only waste money but they slow down performance and strain budgets.
We can prevent these with:
Regular resource monitoring and cost alerts.
Partitioning and query optimisation.
Understanding each service’s pricing model.
Involving cloud engineers or architects before provisioning resources.
Scheduling automatic shutdown for test environments.
Over-Engineering
It’s easy to get excited about new tools, message queues, event-driven pipelines, and real-time dashboards. But not every use case needs complex infrastructure. Many teams fall into the trap of building advanced systems where a simple scheduled script or database table would do.
Over-engineering increases maintenance overhead and makes onboarding harder.
When something breaks, debugging becomes a multi-system task rather than a quick fix.
Start with the simplest working solution. If you outgrow it, you can scale or refactor. A well-structured spreadsheet or a single PostgreSQL instance is sometimes all you need to get started.
Data Modeling Problems
The Chaos of Unstructured Tables
Poor data modelling often leads to flat, unstructured tables without clear relationships or normalisation. This makes querying slow and messy, especially as data grows. Teams end up writing complex joins or dealing with inconsistent fields across sources.
Over time, this creates technical debt. Changes become risky, documentation falls behind, and collaboration gets harder.
Instead, define clear entities and relationships. Apply normalisation where appropriate, and keep your models intuitive. A clean data model saves time for everyone down the line.
The One Big Table Trap
Some teams try to cram everything into a single massive table, metrics, dimensions, and metadata combined. It might work early on, but performance degrades fast.
Scaling becomes difficult, and analysis becomes limited.
A better approach is to use dimensional modelling. Separate fact and dimension tables. It improves performance, simplifies queries, and scales more naturally as the business grows.
Database Misuse
A frequent mistake is querying the production database directly for analytics. At first, it seems efficient, so why copy data when it’s already there?
But this can overload your main systems, slow down core business operations, and risk unintentional data exposure.
The safer approach is to use read replicas or periodically cache data into separate reporting tables. This keeps production stable and analytics efficient.
Process and Methodology Mistakes
A classic mistake in data projects is building something that no one ends up using. This often happens when engineers dive into development without fully understanding the actual business need. You might build an advanced dashboard, a data mart, or a feature store, only to find out later that stakeholders either didn’t ask for it or can't use it as intended.
These misaligned efforts lead to wasted time, delayed value delivery, and sometimes even frustration from teams who feel unheard.
The fix is straightforward: bring stakeholders in early. Before writing code, validate the problem you're solving. Ask what they really need, what decisions the data should support, and how they plan to interact with the final product. This saves time and ensures your work makes an impact.
Security & Compliance Risks
Security often takes a back seat in early-stage development, but neglecting it can have serious consequences. One of the most common mistakes is accidentally exposing sensitive credentials (such as AWS keys or database passwords) in code repositories.
Even a temporary push to a public repo can be picked up by bots almost instantly.
Another risk is the careless handling of user data. Logging personally identifiable information (PII) or failing to mask it during testing can easily lead to compliance issues.
To stay safe:
Use secret management tools (e.g., AWS Secrets Manager, Vault).
Keep credentials out of code.
Regularly audit access logs.
Follow the principle of least privilege.
Security should be built in from the start, not added as a last-minute patch.
Neglect Security and Privacy
Even when teams understand security basics, they sometimes overlook deeper privacy practices. For instance, storing sensitive data without encryption or allowing unrestricted access across departments can lead to data breaches or regulatory violations.
This isn’t just about fines, it can damage trust with users and customers.
A better approach includes:
Implementing Role-Based Access Control (RBAC)
Encrypting data both at rest and in transit
Auditing who can access what and why
Logging all access and regularly reviewing permissions
Strong privacy practices aren’t just for legal compliance, they protect your users and your reputation.
If you're interested in learning more about security fundamentals for data engineers, check out our previous post here:
Data Quality & Governance Issues
Ignoring End Users
Engineers often design data models and dashboards with performance and architecture in mind but forget the people who will use them. If your tables are confusing, undocumented, or overly technical, analysts and business users may avoid them altogether.
This results in low adoption, duplicate efforts, and frustration across teams.
To fix this, think about the end user from the start. Use clear naming, provide examples, and document key tables. Even a simple user guide or data dictionary can make a huge difference in usability.
Missing Documentation
Documentation is often the first thing sacrificed when deadlines loom. But without it, onboarding becomes a guessing game, and knowledge lives only in the heads of a few engineers.
This becomes a serious risk when people change teams or leave the company.
Pipelines stop running, no one knows where logs are stored, and even simple updates become difficult.
Good documentation doesn’t need to be elaborate. A few key things to maintain:
Data flow diagrams.
Pipeline and repo overviews.
Key environment and configuration details.
Tools like Confluence, Notion, or even a well-maintained README can do the job.
Data Profiling Neglect
Another common oversight is failing to properly analyse data before using it. Teams assume data will be clean and structured, only to discover unexpected nulls, inconsistent formats, or weird edge cases after a pipeline fails in production.
This leads to rework, missed deadlines, and avoidable bugs.
Always profile your data sources. Understand distribution, volume, and anomalies before building logic around it. Tools like dbt’s docs and sources, or even basic Pandas profiling in Jupyter notebooks, can help identify problems early.
Push Problems Left, Analytics Right
Sometimes, teams try to fix upstream data issues in their own pipelines. They write complex transformations to handle missing values, inconsistent formats, or wrongly joined tables.
Over time, these “fixes” grow into tangled codebases that are hard to maintain.
The root issue is trying to solve data quality problems too late in the process. The better approach is to push fixes upstream so pipelines stay clean and transformation logic stays focused on analytics, not repairs.
If you're interested in data quality, check out our data quality series here
Communication
Overlooking Non-Technical Feedback
Technical teams sometimes focus so much on implementation that they forget to listen to business users. But without their input, it’s easy to miss the mark. A feature might be technically impressive, but if it doesn't answer the right question, it’s not useful.
When business feedback is ignored, data teams risk building the wrong thing.
Effective communication means inviting feedback early and often. Ask users what’s working, what’s confusing, and what’s missing. Their input is critical to making data products truly valuable.
Talking Technical with Non-Technical Stakeholders
It’s tempting to explain things the way we understand them: schemas, pipelines, distributed systems. But not everyone speaks that language.
If you’re speaking to a marketing manager or product lead, too much technical jargon can lead to confusion or disinterest.
They don’t need the details, they need to know what it means for them.
A good rule: match your level of detail to your audience.
For non-technical users, focus on business value.
For mixed audiences, keep it light and simple.
For technical leaders, go deeper with clear diagrams and details.
You can even test their level of understanding by asking questions. Adjust your explanation based on their responses.
If you're interested in mastering stakeholder communication for data engineers, check out our post here:
Saying Yes Too Much
New data engineers often try to please everyone by agreeing to every data request that comes in. It might work for a while, but soon you’ll find yourself overwhelmed and unable to deliver anything properly.
This overcommitment leads to context switching, burnout, and lower-quality work.
The better approach is to prioritise. Learn to say “no” or “not right now” when needed. Focus on tasks with the highest impact and align with broader goals. Setting boundaries isn’t selfish, it’s smart.
Career Development & Growth Mistakes
Waiting for Management Direction
Many early-career professionals assume their manager will guide their next move, what tools to learn, which projects to join, and how to grow. But the truth is that most managers are focused on team deliverables, not individual roadmaps.
If you wait for someone to steer your career, you may end up stuck.
Instead, take ownership. Reflect on where you want to grow, whether it’s technical depth, leadership, or domain knowledge, and build a personal learning plan.
Technology vs. Concepts Focus
It’s easy to get excited about shiny tools like Snowflake, Spark, dbt, and Kafka. While knowing tools is useful, becoming too tool-focused can leave you with shallow knowledge. When the next hot framework comes along, you might feel like you're starting over.
Strong data engineers invest in core concepts: how data flows, how to model it, how to optimise queries, and how to ensure quality.
Balance your learning, get familiar with tools, and master the fundamentals.
Neglecting Professional Network
Some engineers focus entirely on their work and avoid networking altogether. It might feel unnecessary, especially in technical roles.
The network is a powerful tool, not just for job opportunities but for learning and perspective.
Colleagues, community groups, meetups and online spaces like LinkedIn, Slack or Discord channels can connect you with people solving similar problems. They can offer ideas, feedback, or support when you hit a roadblock.
Work-Life Imbalance
In fast-paced environments or early in your career, it’s easy to fall into the trap of always saying “yes,” working late nights, and putting work ahead of everything else. While dedication is valuable, this approach isn’t sustainable.
Without boundaries, burnout is inevitable. Productivity drops, health suffers, and enthusiasm fades.
Set clear working hours. Take breaks. Make time for hobbies, relationships, and rest. You'll be more effective and happier when you're balanced.
Best Practices for Junior Data Engineers
Start Simple, Iterate Later: Don't focus on performance or scalability too early.
Focus on Business Needs: Understand the problem you're solving, not just the technology.
Master the Fundamentals: SQL, data modeling, and understanding pipelines are critical.
Learn from Mistakes: Don't be afraid to make errors; they're valuable learning opportunities.
Collaborate Across Teams: Work with data scientists, analysts, and product teams.
Be Adaptable: Stay flexible and open to learning new tools and techniques.
By avoiding these common mistakes and following best practices, data engineers can build more reliable, efficient, and valuable data systems that truly serve business needs.
Conclusion
In this post, we tried to share some of the most common mistakes made in data engineering that we have observed.
We believe that by understanding what can go wrong, the impact these mistakes can have, and how to prevent them, data professionals can build more reliable, scalable, and user-focused systems.
Whether you’re just starting out or looking to sharpen your skills, these lessons offer practical guidance for growing in the field of data engineering.
We Value Your Feedback
If you have any feedback, suggestions, or additional topics you’d like us to cover, please share them with us. We’d love to hear from you!
If you want to learn more about the Data Engineering Lifecycle, check out our series [here].
Preparing for a Data Engineering interview? Explore our interview prep series [here].
Interested in SQL optimisation? Dive into our SQL series [here].
Resources
15 Mistakes That Make You a Better Data Engineer by Jagadesh Jamjala
5 Mistakes New Data Engineers Make by
What are some common data engineering mistakes you’ve seen in your career? by Reddit Community.
Worst Data Engineering Mistake you’ve seen? Reddit Community.
5 Critical Mistakes Every Data Engineer Must Avoid for Career Success by
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Great article! One classic data profiling technique, but highly efficient, is “staging tables”. It’s covered in Ralph Kimble’s book. The idea is simple: for each table or data resource, we capture information like the table name, row count, size in MB or bytes, expected volume growth, the type of ETL job, and so on.
Very detailed and helpful article.