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. 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.

3. 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.

4. 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?"

The word cloud above highlights the frequency of terms found in interview reports and job descriptions. Notice the prominence of SQL, Snowflake, dbt, and Product. This confirms that while general engineering skills are valued, specific proficiency in their chosen stack and a product-oriented mindset are the biggest differentiators.

5. Key Responsibilities

As a Data Engineer at Workiva, your daily work will revolve around enabling the flow of high-value data. You will not just be maintaining pipelines; you will be architecting the solutions that allow the product to scale.

Your primary responsibility will be domain solution leadership. This means you will architect and build high-performance data solutions that directly power customer-facing features. You will utilize the internal data platform—comprising Snowflake, dbt, and Kafka—to deliver reliable data products. You will often work independently, leading complex projects from the initial discovery phase with stakeholders all the way to production deployment and monitoring.

Collaboration is equally critical. You will work embedded with or alongside Application Engineering teams. In this capacity, you will advocate for upstream data quality, ensuring that the applications are built in a way that supports downstream data needs. You act as a "Lead Customer" for the internal Platform team, identifying gaps in capabilities and helping to shape the strategic roadmap.

Operational excellence is the third pillar of your role. You will design resilient pipelines using tools like DLT (Data Load Tool) and Snowpipe to handle enterprise-scale workloads. You will own the domain’s dbt layer, ensuring code is modular, tested, and optimized. Furthermore, you will be expected to mentor senior and mid-level engineers, elevating the team's technical standard through code reviews and design documentation.

6. Role Requirements & Qualifications

Candidates who succeed in this process typically possess a blend of specific technical skills and broad engineering experience.

Must-have skills

• Advanced SQL & Python: You must be able to write production-grade code in both.
• Snowflake & dbt Expertise: Deep understanding of these tools is essential for the daily workflow.
• Data Modeling: Proven experience designing schemas for both analytical (OLAP) and application-support use cases.
• Communication: The ability to translate technical data concepts to non-technical product owners.

Nice-to-have skills

• Streaming Technologies: Experience with Kafka or similar event-streaming platforms.
• Ingestion Frameworks: Familiarity with DLT or Snowpipe.
• SaaS Background: Experience working in a product-led SaaS environment, particularly in FinTech or regulated industries.
• CI/CD Implementation: Experience automating data workflows and testing.

7. 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.

8. Frequently Asked Questions

Q: How difficult is the technical assessment? The technical assessment is generally described as practical and fair. It focuses on SQL proficiency and logical problem-solving rather than obscure algorithms. If you are comfortable with joins, aggregations, and window functions, you will be well-prepared.

Q: Is this a remote role? Yes, Workiva is very remote-friendly. The job descriptions explicitly state that they support employees working where they work best, provided they have a reliable internet connection. However, occasional travel for "team jams" or meetings may be required.

Q: What is the culture like for Data Engineers? The culture is highly collaborative. Data Engineers are not siloed; they are expected to work closely with application developers and product managers. There is a strong emphasis on "Data-as-a-Product," meaning your work is treated with the same rigor as user-facing software.

Q: How much preparation time do I need? Most candidates benefit from 1-2 weeks of focused preparation. Spend the majority of your time brushing up on advanced SQL and reviewing dbt and Snowflake documentation to ensure you can discuss their specific features confidently.

Q: What differentiates a Senior/Staff candidate from a mid-level one? Senior and Staff candidates are expected to drive strategy. You shouldn't just build what is asked; you should be able to challenge requirements, propose architectural improvements, and mentor others. The ability to navigate ambiguity is a key differentiator.

9. Other General Tips

Know the Business Context Workiva operates in the financial reporting and compliance space. This means accuracy, security, and governance are not optional—they are the product. When answering questions, always prioritize data integrity and security. Mentioning "audit trails" or "governance" shows you understand their world.

Prepare Questions for the Manager In the "relaxed discussion" with the manager, having insightful questions is crucial. Ask about the relationship between the Platform team and the embedded domain teams. Ask how they measure the success of a data product. This demonstrates your strategic thinking.

Highlight "Self-Service" A key goal in the job description is building a "self-service data platform." Prepare examples of how you have built tools or documentation that allowed other teams to access data without your constant intervention.

Be Honest About Gaps If you haven't used a specific tool like DLT or Kafka, admit it, but explain how your experience with similar tools (like Fivetran or Kinesis) translates. Workiva values adaptability and the ability to learn quickly over perfect keyword matching.

10. Summary & Next Steps

The Data Engineer role at Workiva is a high-impact position that sits at the intersection of modern infrastructure and critical business intelligence. It offers the opportunity to work with a cutting-edge stack—including Snowflake, dbt, and Kafka—while solving complex problems in the financial reporting domain. The company values engineers who can think like product owners, ensuring that data is not only accurate but also accessible and valuable to the end-user.

To succeed, focus your preparation on mastering SQL and understanding the architectural patterns of the modern data stack. Be prepared to demonstrate how you have led projects, influenced stakeholders, and delivered robust data solutions in the past. Approach the interview with curiosity and a collaborative mindset; show them that you are not just a coder, but a partner in building their data ecosystem.

The salary data above provides a view of the compensation range for this role. Note that Workiva differentiates between levels (e.g., Staff vs. Director), and your offer will depend heavily on your location, experience level, and performance during the interview loop.

You have the skills to excel in this process. Review your SQL, reflect on your past projects, and go into the interview ready to discuss how you can help Workiva build the future of connected reporting. Good luck!