Next Steps for Staff+ Data Engineering
Cement your role as indispensable by making data visible, repeatable, and persuasive.
We'll cover the following...
Here’s the ladder from basic logging to steering the roadmap with evidence.
Structured logging: Add event logs to your features and define an event dictionary so the team knows what they mean.
Starter tools: Python logging, Log4j (Java), Winston (Node.js), Datadog for hosted logs
Python logging, Log4j (Java), Winston (Node.js), and Datadog for hosted logs.
Pipelines: Learn how to build pipelines that land data in a warehouse where it can be queried.
Starter tools: dbt Core (free), Airbyte, Fivetran (cloud ETL/ELT), BigQuery free tier
Streaming vs. batch: Experiment with both processing modes.
Starter tools: Apache Kafka (local Docker setup), Spark Structured Streaming, Google Pub/Sub
Experimentation: Run A/B tests, track guardrails, and present results in plain language.
Starter tools: Amplitude (free tier), Optimizely (developer sandbox), or roll your own with feature flags (e.g., LaunchDarkly, Unleash open source).
Climb these four stages, and you become the engineer who steers product direction with evidence.
Where to learn more
Start getting hands-on with these skills with our popular courses
Data Engineering Foundations in Python: Understand the entire data life cycle and get hands-on experience building data pipelines using different Python, Kafka, PySpark, Airflow, dbt, and more.
Learn Data Engineering: Learn to handle structured and unstructured data with NoSQL and MongoDB, work with big data using Hadoop, Spark, and Kafka.
Data Science Handbook: Dive into the fundamentals of data science for data-driven decision-making.
👉 If you’re working in Rust, prove your data skills by building this portfolio-worthy project: Rust Data Engineering: Building High-Performance EDA.
Now let’s move on to how to keep data out of the wrong hands with "Security Without Drama."