Week 18/34: Data Pipelines and Workflow Orchestration for Data Engineering Interviews (Part #3)
Understanding Data Workflow best practices and a Hands-On ELT Pipeline using dbt and Dagster
In our first two posts, we laid the foundation for understanding data orchestration tools and explored how platforms like Apache Airflow and Dagster operate in real-world data teams. We covered:
Fundamentals of data pipeline orchestration.
Key terms and concepts.
Roles involved in orchestration.
Core problems are solved by orchestration.
A real-world example.
What is Apache Airflow and how does it work?
What Dagster is and how it works.
The differences and trade-offs between tools.
Orchestration tools-related interview questions.
For the previous posts of this interview series, check here: [Data Engineering Interview Preparation Series]1
Now, it's time to shift from theory to practice. In this post, we’ll walk through a complete hands-on ELT pipeline implementation and demonstrate how to orchestrate it using Dagster.
We will:
Discuss workflow best practices in production environments.
Build a production-level pipeline that:
Extracts data from a public API.
Loads the raw data into a local Postgresql database.
Transforms the data using dbt (Data Build Tool).
Orchestrates the entire process with Dagster.
Finally, we’ll schedule the pipeline to run weekly
By the end, we’ll have a functional, modular ELT pipeline.
All code and resources are available in the GitHub repository: [link]2
Almost all data engineering interviews include a technical assessment. While orchestration is often not part of the hands-on task, it frequently comes up during the verbal or system design discussions. Demonstrating real-world understanding through hands-on experience can significantly strengthen your answers. Being able to showcase your own projects and explain how you’ve implemented orchestration is a powerful way to validate your knowledge and stand out in interviews.
Workflow Best Practices in Production Environments
Building a working pipeline is one thing, but ensuring it's reliable, observable, and maintainable at scale is what sets apart a production-ready solution from a demo project.
Here are some of the most important best practices to follow when designing data workflows:
1. Modularity and Reusability
Workflows are most effective when broken into small and logical components. Each task should perform a single, well-defined function. This modularity allows easier testing, debugging, and reusability across projects.
2. Separation of Concerns
Separating orchestration logic (e.g., scheduling, retries) from transformation logic (e.g., SQL models or scripts) leads to cleaner, more maintainable pipelines. This ensures that operational concerns don’t interfere with business logic, reducing complexity over time.
3. Idempotency
In production environments, tasks often re-run due to retries or schedule overlaps. Ensuring that each step is idempotent so that it produces the same result regardless of how many times it’s executed. This helps prevent data corruption and duplication.
4. Observability and Logging
A well-designed pipeline includes structured logs, visibility into task status, and traceability for each run. Tools like Dagster provide built-in observability through run timelines, step durations, and log outputs. This is very critical for diagnosing failures or verifying success.
5. Failure Handling and Alerting
Workflows should account for failure scenarios through retries, conditional logic, and proactive alerting. Whether via web hooks, messaging tools, or dashboards, timely failure notifications allow fast resolution and system reliability.
That said, alert fatigue is also a real risk that too many or repetitive alerts can lead to critical issues being overlooked. Alerts should be meaningful, actionable, and reserved for situations that genuinely require attention. If a workflow frequently triggers alerts, it's often a signal that something upstream needs improvement.
6. Data Quality Checks
Validating data at key stages helps catch schema issues, missing values, or unexpected anomalies. Tools like dbt offer built-in testing capabilities that align with production-grade quality assurance.
For more details about data quality checks, you can check out our below post.
7. Scheduling and Dependency Management
Efficient pipelines run on predictable schedules and only proceed when upstream dependencies are met. Leveraging orchestration tools’ scheduling and DAG-based execution ensures pipelines are coordinated, traceable, and fail-safe.
These principles reflect real-world expectations and help structure pipelines that are not only functional but maintainable and scalable.