10 Mistakes to Avoid on Your Path to Becoming a Data Engineer

WSDA News | May 8, 2025

Entering the data engineering field is exciting, but it’s easy to stumble over common challenges that can slow progress and sap morale. Many newcomers only learn these lessons the hard way—after pipelines break, performance lags, or stakeholders lose confidence. Whether you’re launching your first production workflow or guiding a junior engineer, steering clear of these ten missteps will set you on a smoother path.

1. Skipping Thoughtful Data Modeling

Flat tables may seem quick to build, but they become a maintenance nightmare as datasets grow. Without a clear schema, queries slow to a crawl and relationships get lost in the noise.

What to do instead:

• Sketch out fact and dimension tables on paper first.

• Define primary and foreign keys that mirror real-world entities.

• Balance normalization and denormalization based on query patterns.

2. Overlooking Data Validation

Pipelines can appear to run successfully yet silently drop or corrupt records. Missing or malformed fields in production data often surface only when dashboards look wrong.

What to do instead:

• Enforce schemas at ingestion (for example, with JSON Schema or Apache Avro).

• Insert lightweight checks for nulls, ranges, and expected formats.

• Send failing records to a quarantine table for review, rather than letting them vanish.

3. Embedding Secrets and Paths in Code

Hard-coded database credentials, file locations, or API keys make deployments fragile and insecure. Once code moves between environments, jobs break and sensitive information may be exposed.

What to do instead:

• Read all configuration values from environment variables or external files.

• Use a dedicated secrets manager (AWS Secrets Manager, HashiCorp Vault).

• Keep your codebase free of any direct references to passwords or service endpoints.

4. Forgoing Integration Testing

Unit tests for individual functions catch obvious bugs—but they won’t reveal mismatches between pipeline stages. Incomplete end-to-end coverage means failures show up mid-release.

What to do instead:

• Create a small, representative dataset for automated integration tests.

• Run those tests on every pull request in your CI/CD pipeline.

• Treat pipelines like applications: if any stage fails, the build should block.

5. Not Instrumenting Monitoring and Alerts

A job that fails quietly over the weekend can derail Monday morning reports. Without real-time visibility, minor issues escalate into crises.

What to do instead:

• Emit structured logs and metrics from each task (ingestion counts, error rates).

• Configure alerts for skipped runs, latency spikes, or data volume anomalies.

• Build simple dashboards that show pipeline health at a glance.

6. Overengineering Early Projects

Jumping to distributed frameworks or microservices before they’re needed wastes time on cluster management rather than business logic. Simpler tools often suffice for small datasets.

What to do instead:

• Prototype with Pandas or a single-node Spark session.

• Profile data volumes and performance early.

• Scale out only when you hit measurable bottlenecks.

7. Assuming Tools Solve All Problems

Falling in love with the latest messaging queue or orchestration framework won’t fix a broken data strategy. Technology without solid foundations leads to brittle systems.

What to do instead:

• Master core concepts (batch vs. stream processing, consistency models) before picking a tool.

• Choose technologies based on requirements, not hype.

• Validate that each addition simplifies operations rather than complicating them.

8. Building Without Stakeholder Input

A technically perfect pipeline means little if it doesn’t answer real questions. Engineers who skip feedback loops deliver results that go unused.

What to do instead:

• Conduct brief interviews with end users before designing.

• Demo initial prototypes and iterate based on comments.

• Tie every metric and transformation back to a decision it supports.

9. Neglecting Version Control for Code and Data

Overwriting transformation scripts or data files without backups makes reproducing past reports a guessing game. Rollbacks become painful and time-consuming.

What to do instead:

• Store all ETL scripts and configuration in Git.

• Leverage data-versioning frameworks (Delta Lake, Apache Iceberg) to snapshot tables.

• Tag releases so you can reconstruct historical pipelines on demand.

10. Writing Code Without Documentation

Well-structured code speaks for itself—until someone else needs to extend or troubleshoot it. Lack of README files and annotations raises the barrier for collaboration.

What to do instead:

• Include a top-level README describing pipeline inputs, outputs, and dependencies.

• Document tricky business rules or edge cases alongside your code.

• Encourage team members to leave brief comments on pull requests explaining their changes.

Why It Matters

Avoiding these common missteps accelerates your ramp-up time, reduces burnout, and builds credibility with stakeholders. Clean schemas and solid tests mean fewer production fires; thoughtful monitoring and collaboration lead to insights that genuinely drive business decisions. By getting the fundamentals right, you spend less time fixing problems and more time delivering value.

Your Next Steps

1. Assess Your Current Projects

• Identify any pipelines lacking schema definitions or validation.

• Flag hard-coded credentials or missing integration tests.

2. Implement One Improvement at a Time

• Prioritize the area causing the biggest pain—whether it’s reliability, performance, or security.

• Roll out small changes and measure their impact.

3. Establish a Feedback Loop

• Schedule regular check-ins with data consumers.

• Demo new features early and adjust based on their needs.

4. Document and Share

• Write clear READMEs and maintain version control for both code and data.

• Share lessons learned with your team to foster a culture of continuous improvement.

Stepping into data engineering with these practices in place sets you up for long-term success. Remember: it’s not just about writing code—it’s about building reliable systems, collaborating effectively, and creating insights that matter.

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