1. What is a Data Engineer?

At Workiva, the role of a Data Engineer is pivotal to the integrity of the platform. Workiva powers complex financial reporting, ESG (Environmental, Social, and Governance) disclosures, and audit solutions for thousands of global enterprises. Consequently, data engineering here is not simply about moving bits from point A to point B; it is about constructing the "source of truth" that major corporations rely on for regulatory compliance and strategic decision-making.

You will act as a strategic partner who bridges the gap between infrastructure and application. While the Platform team may build the underlying architecture, you are often the "expert consumer" who transforms that infrastructure into tangible solutions. You will design secure, scalable, and self-service data products that empower internal application teams and external customers. This role requires a blend of technical rigor—utilizing tools like Snowflake, dbt, and Kafka—and a product-focused mindset to ensure data quality and observability are embedded into the user experience.

2. Common Interview Questions

The following questions are representative of what you might face. They are drawn from candidate data and the specific requirements of the role. While you should not memorize answers, you should use these to practice your structuring and storytelling.

Technical & Domain Knowledge

• "What is the difference between a CTE and a temporary table, and when would you use one over the other in Snowflake?"
• "How do you handle backfilling data for a large incremental model in dbt without causing downtime?"
• "Describe a time you had to optimize a SQL query that was timing out. What was your approach?"
• "How do you implement data quality tests in your pipeline? Give an example of a 'blocking' test."

System Design & Architecture

• "We need to ingest data from a third-party API that has strict rate limits. How would you design the ingestion layer?"
• "Walk me through how you would design a multi-tenant data architecture where customer data must be strictly isolated."
• "Compare batch processing vs. stream processing. When would you choose Kafka over a standard batch load for a reporting feature?"

Behavioral & Leadership

• "Tell me about a time you had to push back on a Product Manager's request because of technical debt or data quality concerns."
• "Describe a situation where you had to mentor a junior engineer who was struggling with a complex data concept."
• "How do you handle a situation where an upstream application team changes a schema without notifying you, breaking your pipeline?"

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

3. Getting Ready for Your Interviews

Preparation for the Workiva interview process requires a shift in mindset from purely technical execution to "Data-as-a-Product." You should approach your preparation by focusing on how your technical decisions drive business value and how you collaborate with non-technical stakeholders.

You will be evaluated on the following key criteria:

Technical Proficiency & Modern Stack Mastery Interviews will heavily test your hands-on capability with the modern data stack. Specifically, you must demonstrate deep expertise in SQL, data modeling (for both OLAP and high-concurrency use cases), and transformation workflows using dbt. Expect to discuss how you optimize pipelines for cost and performance within Snowflake.

Product-Driven Engineering Workiva looks for engineers who understand the "why" behind the data. You will be assessed on your ability to translate vague customer needs into concrete technical requirements. You should be prepared to discuss how you treat data as a product, ensuring high availability, latency management, and strict governance.

Communication & Stakeholder Management A significant portion of your role involves "evangelizing" data best practices to application engineering teams. Interviewers will look for evidence that you can influence roadmaps, mentor junior engineers, and articulate complex data strategies to product managers and business leaders.

Cultural Alignment Workiva values innovation, accountability, and a collaborative spirit. You will be evaluated on how you navigate ambiguity and how you foster a culture of inclusion. Be ready to share examples of how you have taken ownership of end-to-end delivery with minimal supervision.

4. Interview Process Overview

The interview process at Workiva is designed to be thorough yet relaxed, focusing on genuine conversation rather than high-pressure interrogation. The process generally begins with a recruiter screen to align on your background and interests. Following this, you will likely proceed to a technical screen or a discussion with a hiring manager. This stage often involves a blend of behavioral questions and a high-level review of your technical experiences.

Successful candidates then move to a more in-depth loop, which may be split into specific sessions focusing on technical assessment and team fit. You should expect a practical SQL assessment that tests your ability to manipulate data and solve logic problems in real-time. Additionally, there will be deep-dive sessions regarding system design, where you will architect solutions using their specific stack components (like Kafka and Snowflake). The atmosphere is typically described as positive and conversational, with interviewers genuinely interested in your thought process.

The timeline above illustrates the typical progression from application to offer. Note that the Technical Assessment often serves as a gateway to the final rounds. You should use this visualization to pace your preparation; ensure your SQL skills are sharp early on, then transition your focus to system design and behavioral stories for the later stages.

5. Deep Dive into Evaluation Areas

To succeed, you must demonstrate competency across several specific domains. Workiva’s data ecosystem is mature, and they expect candidates to understand the nuances of the tools they use.

SQL and Data Modeling

This is the foundation of the assessment. You must show that you can write performant, clean SQL. Beyond syntax, you will be evaluated on your ability to model data for scalability.

Be ready to go over:

• Complex Joins and Window Functions – Writing queries that handle complex logic without sacrificing performance.
• Dimensional Modeling – Understanding Star vs. Snowflake schemas and when to denormalize for performance.
• Data Quality – How you implement tests and validation logic within your SQL or dbt models.

Example questions or scenarios:

• "Given a table of customer transactions, write a query to find the top 3 products sold per region for the last quarter."
• "How would you debug a query that has suddenly become 10x slower?"
• "Design a schema to support a financial reporting dashboard that requires real-time updates."

The Modern Data Stack (Snowflake & dbt)

Workiva relies heavily on Snowflake for storage and compute, and dbt for transformation. You need to speak the language of these tools.

Be ready to go over:

• Snowflake Architecture – Understanding micro-partitions, clustering keys, and virtual warehouses.
• dbt Best Practices – Modularizing code, using macros, and managing incremental models.
• Pipeline Orchestration – How you manage dependencies and ensure reliable delivery.
• Advanced concepts – Cost optimization strategies in Snowflake and custom materializations in dbt.

Example questions or scenarios:

• "Explain how you would structure a dbt project for a multi-tenant application."
• "We are seeing high credit usage in Snowflake. What steps would you take to identify and reduce the cost?"
• "How do you handle schema evolution in a production pipeline?"

System Design & Streaming

For senior roles, you will be expected to architect end-to-end solutions that include data ingestion and serving.

Be ready to go over:

• Ingestion Patterns – Batch vs. Streaming (specifically Kafka).
• Latency vs. Accuracy – Making trade-offs for customer-facing features.
• Observability – How you monitor pipeline health and alert on failures.

Example questions or scenarios:

• "Design a data pipeline that ingests user activity logs from Kafka and makes them available for analytics within 5 minutes."
• "How would you architect a system to ensure data consistency across internal ERP systems and the data warehouse?"