Tegna Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Tegna? The Tegna Data Engineer interview process typically spans a range of question topics and evaluates skills in areas like ETL pipeline design, data modeling, SQL and Python proficiency, and stakeholder communication. Interview preparation is especially important for this role at Tegna, where Data Engineers are expected to architect scalable data solutions, ensure data quality and accessibility for diverse business use cases, and clearly present technical concepts to both technical and non-technical audiences. Success in the interview relies on demonstrating not only technical depth but also adaptability in solving real-world data challenges and collaborating across teams.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Engineer positions at Tegna.
• Gain insights into Tegna’s Data Engineer interview structure and process.
• Practice real Tegna Data Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Tegna Data Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Tegna is a leading media company specializing in broadcast television and digital media, operating dozens of local TV stations across the United States. The company delivers news, entertainment, and information to diverse audiences, emphasizing innovation and community engagement. Tegna is committed to leveraging data and technology to enhance content delivery and user experience. As a Data Engineer, you will play a crucial role in optimizing data infrastructure and supporting Tegna’s mission to provide reliable, impactful media services in a rapidly evolving digital landscape.

As a Data Engineer at Tegna, you are responsible for designing, building, and maintaining the data infrastructure that supports the company’s digital media and broadcasting operations. You will work with large datasets, developing ETL pipelines and data models to ensure the reliable flow and accessibility of information across various teams, including analytics, product, and business intelligence. Your role involves optimizing data storage solutions, ensuring data quality, and collaborating with stakeholders to deliver scalable solutions that drive data-driven decision-making. By enabling robust data capabilities, you help Tegna enhance content delivery, audience insights, and overall operational efficiency.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough evaluation of your resume and application materials by Tegna’s data engineering hiring team. Here, they look for demonstrated experience with large-scale data pipelines, proficiency in SQL and Python, and familiarity with cloud platforms and ETL processes. Emphasis is placed on your ability to design, implement, and optimize robust data solutions for varied business needs. Prepare by ensuring your resume highlights specific achievements in data engineering, scalable system design, and data quality initiatives.

2.2 Stage 2: Recruiter Screen

A recruiter will reach out for an initial phone screening, typically lasting 30 minutes. This conversation focuses on your interest in Tegna, motivation for pursuing the data engineer role, and a high-level overview of your technical background. Expect to discuss your experience with data infrastructure, stakeholder communication, and your approach to data-driven problem solving. Preparation should center on articulating your career narrative, relevant skills, and alignment with Tegna’s mission.

2.3 Stage 3: Technical/Case/Skills Round

This stage consists of one or more interviews led by Tegna’s data engineering managers or senior engineers. You’ll be asked to solve technical problems related to ETL pipeline design, data warehouse architecture, and large-scale data processing. System design questions may cover topics like building scalable ingestion pipelines, transforming messy datasets, and optimizing SQL queries for billions of rows. You may also be given case studies or whiteboard exercises to demonstrate your ability to architect solutions, address data quality issues, and choose appropriate technologies (e.g., Python vs. SQL). Preparation should involve reviewing your experience with real-time and batch data processing, data cleaning, and pipeline optimization.

2.4 Stage 4: Behavioral Interview

Behavioral interviews are conducted by data team leads or cross-functional partners and focus on your ability to collaborate, communicate complex data insights, and manage stakeholder expectations. You’ll be asked to reflect on past projects, describe how you overcame hurdles in data initiatives, and explain how you make technical concepts accessible to non-technical audiences. Prepare by developing stories that showcase your adaptability, teamwork, and strategic communication skills in fast-paced, data-driven environments.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves a series of onsite or virtual interviews with Tegna’s data leadership, engineers, and business stakeholders. This round may include deeper dives into technical system design, live coding, and scenario-based discussions about data pipeline reliability, scalability, and business impact. You may be asked to present solutions, justify technology choices, and navigate real-world data challenges. Preparation should focus on synthesizing your technical expertise with business acumen, demonstrating your approach to cross-functional collaboration, and showcasing your ability to deliver actionable insights.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, Tegna’s HR team will present an offer detailing compensation, benefits, and start date. This stage is your opportunity to discuss package details, clarify expectations, and negotiate terms to align with your career goals. Preparation should include researching industry standards and being ready to articulate your value based on interview performance.

2.7 Average Timeline

The Tegna Data Engineer interview process typically spans 3-5 weeks from initial application to final offer. Fast-track candidates may complete the process in as little as 2-3 weeks, while the standard pace allows about a week between each stage to accommodate scheduling and feedback. Technical rounds and onsite interviews may be grouped closely for high-priority candidates, but the behavioral and offer stages can vary based on team availability and negotiation complexity.

Now, let’s dive into the specific interview questions Tegna has asked in recent Data Engineer interviews.

3.1 Data Pipeline & ETL Design

Expect questions that assess your ability to architect, build, and optimize scalable data pipelines and ETL processes. Focus on demonstrating your understanding of data ingestion, transformation, and orchestration, as well as your approach to handling real-world data challenges.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe your approach to handling diverse data formats, ensuring data consistency, and managing schema evolution. Emphasize scalability, error handling, and monitoring strategies.

3.1.2 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Outline your process for validating, transforming, and storing large CSV files, including how you would automate reporting and handle data quality issues.

3.1.3 Let's say that you're in charge of getting payment data into your internal data warehouse.
Explain your end-to-end pipeline design, covering extraction, transformation, loading, and monitoring. Highlight how you ensure data integrity and minimize latency.

3.1.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Discuss the technologies and architecture you would use for ingestion, processing, storage, and serving predictions. Address scalability and real-time considerations.

3.1.5 Redesign batch ingestion to real-time streaming for financial transactions.
Explain how you would move from batch to streaming data processing, including the tools and frameworks you’d use and how you’d ensure data reliability and low latency.

3.2 Data Warehousing & System Design

These questions gauge your expertise in designing data storage solutions that are reliable, performant, and aligned with business needs. Be ready to discuss trade-offs in architecture and your approach to scaling and maintaining data systems.

3.2.1 Design a data warehouse for a new online retailer.
Detail your schema design, partitioning strategy, and approach to supporting both analytical and operational queries.

3.2.2 System design for a digital classroom service.
Describe the core components, data flows, and scalability concerns. Address data privacy and integration with external systems.

3.2.3 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time
Explain how you would structure data storage and streaming to support real-time analytics and visualization.

3.2.4 Designing a pipeline for ingesting media to built-in search within LinkedIn
Discuss your approach to indexing, search optimization, and handling large-scale unstructured data.

3.3 Data Quality & Cleaning

Data engineers must ensure the reliability and usability of data. These questions focus on your strategies for cleaning, validating, and maintaining high data quality across complex datasets.

3.3.1 Ensuring data quality within a complex ETL setup
Share your methods for validating data at each stage of an ETL pipeline and how you monitor for anomalies.

3.3.2 Describing a real-world data cleaning and organization project
Walk through a specific example, detailing the tools and processes you used to clean, organize, and document data.

3.3.3 How would you approach improving the quality of airline data?
Discuss your framework for profiling, identifying, and remediating data quality issues at scale.

3.3.4 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Explain how you’d standardize formats, automate corrections, and ensure accurate downstream analytics.

3.4 SQL & Programming

You’ll be tested on your SQL and programming skills for manipulating and analyzing large datasets. Expect to demonstrate both technical proficiency and your ability to select the right tool for the job.

3.4.1 python-vs-sql
Discuss scenarios where you’d prefer Python or SQL for data tasks, and justify your choices based on scalability, maintainability, and performance.

3.4.2 t Value via SQL
Describe how you would calculate statistical values using SQL, including any challenges with large datasets or complex calculations.

3.4.3 Write a query to compute the average time it takes for each user to respond to the previous system message
Show how you’d use window functions to align events and compute time differences efficiently.

3.5 Communication, Visualization & Stakeholder Engagement

Data engineers are expected to communicate complex technical findings to a variety of audiences. These questions assess your ability to translate data insights into actionable business recommendations and collaborate with non-technical stakeholders.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Explain how you adjust your communication style and visualization techniques based on stakeholder needs.

3.5.2 Demystifying data for non-technical users through visualization and clear communication
Share your approach to making data accessible and actionable for business users.

3.5.3 Making data-driven insights actionable for those without technical expertise
Describe how you translate technical analysis into business impact and ensure your recommendations are understood.

3.5.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Discuss your process for identifying misalignments early and aligning on project goals.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a scenario where your analysis directly influenced a business outcome. Briefly describe the context, your approach, and the measurable impact.

3.6.2 Describe a challenging data project and how you handled it.
Choose a technically complex project, outline the main obstacles, and discuss the strategies you used to overcome them.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain how you clarify objectives through stakeholder engagement and iterative prototyping.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Share how you identified the communication gap, adapted your approach, and ensured mutual understanding.

3.6.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Discuss your strategy for quantifying additional work, re-prioritizing, and maintaining project focus.

3.6.6 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Detail your process for facilitating consensus, documenting definitions, and updating data models.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe how you built credibility, presented evidence, and navigated organizational dynamics.

3.6.8 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Explain the tools and processes you implemented and the long-term impact on data reliability.

3.6.9 Describe a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Share how you assessed the missingness, communicated limitations, and enabled data-driven decisions despite imperfect data.

4.1 Company-specific tips:

Deepen your understanding of Tegna’s media landscape and how data enables their mission. Study Tegna’s digital transformation initiatives, their focus on content delivery, and how they leverage data to drive audience engagement and operational efficiency. This knowledge will help you contextualize your technical answers and demonstrate your alignment with the company’s goals.

Research Tegna’s diverse portfolio, including local broadcast stations and digital products. Be ready to discuss how scalable data solutions can support both legacy broadcast operations and innovative digital media projects. Show that you can adapt your data engineering approach to the unique requirements of a media company.

Familiarize yourself with Tegna’s emphasis on cross-team collaboration. Prepare to highlight examples of working with stakeholders from editorial, product, and analytics teams. Emphasize your ability to translate complex technical concepts into actionable insights for non-technical audiences.

Keep up to date with trends in media technology, such as real-time analytics, audience segmentation, and content personalization. Reference relevant advancements in your discussions to showcase your passion for the industry and your readiness to contribute to Tegna’s evolving data strategy.

4.2 Role-specific tips:

Demonstrate your expertise in designing and optimizing ETL pipelines. Be prepared to discuss your approach to ingesting, transforming, and orchestrating large volumes of heterogeneous data—particularly from sources common in media environments, such as streaming logs, audience metrics, and advertising data. Highlight your ability to ensure data consistency, handle schema evolution, and implement robust error handling and monitoring.

Showcase your proficiency in both SQL and Python for data engineering tasks. Practice explaining when you would choose one over the other for specific challenges, such as large-scale data processing, complex transformations, or automation. Be ready to write and optimize SQL queries involving window functions, aggregations, and time-based calculations, as well as Python scripts for data cleaning and pipeline automation.

Prepare to discuss your experience with modern data warehousing and storage solutions. Highlight your knowledge of cloud platforms, partitioning strategies, and designing schemas that support both operational and analytical workloads. Be explicit about how you balance scalability, performance, and cost-effectiveness in your system designs.

Emphasize your strategies for maintaining data quality and reliability. Describe your methods for validating data at each stage of an ETL pipeline, monitoring for anomalies, and remediating data quality issues at scale. Share real-world examples of automating data quality checks and documenting processes to prevent recurring issues.

Illustrate your ability to communicate technical concepts clearly to both technical and non-technical stakeholders. Practice explaining complex pipeline architectures, data models, and analytical findings in a way that is accessible and actionable. Prepare stories that demonstrate how you’ve resolved misaligned expectations, negotiated scope, and built consensus on data definitions or project priorities.

Show your adaptability in ambiguous or fast-changing environments. Be ready to discuss how you handle unclear requirements, gather feedback iteratively, and adjust your solutions based on stakeholder input. Use examples that reflect your flexibility and commitment to delivering data-driven value, even when facing shifting business needs or incomplete data.

Finally, highlight your business acumen and the impact of your work. Frame your technical achievements in terms of how they enabled better decision-making, improved operational efficiency, or enhanced user experience. Tegna values data engineers who not only build robust systems but also drive measurable business outcomes.

5.1 How hard is the Tegna Data Engineer interview?
The Tegna Data Engineer interview is considered challenging, especially for candidates without prior experience in media or large-scale data environments. You’ll be expected to demonstrate deep technical knowledge in ETL pipeline design, data modeling, SQL and Python expertise, and the ability to communicate technical solutions to non-technical stakeholders. Questions often focus on real-world scenarios relevant to Tegna’s broadcast and digital operations, so a strong grasp of both technical and business contexts is essential.

5.2 How many interview rounds does Tegna have for Data Engineer?
The Tegna Data Engineer interview process typically includes five main rounds: an application and resume review, a recruiter screen, one or more technical/case interviews, a behavioral interview, and a final onsite or virtual round with data leadership and cross-functional partners. Each stage is designed to assess both your technical depth and your collaborative, problem-solving skills.

5.3 Does Tegna ask for take-home assignments for Data Engineer?
While Tegna’s process may occasionally include a take-home assignment, most technical evaluation is done through live interviews and case studies. If assigned, take-home work will likely focus on designing or optimizing an ETL pipeline, data modeling, or solving a real-world data quality problem relevant to Tegna’s business.

5.4 What skills are required for the Tegna Data Engineer?
Key skills for Tegna Data Engineers include expertise in ETL pipeline design, strong SQL and Python programming, data modeling, and experience with cloud data platforms. You’ll also need a solid understanding of data quality assurance, data warehousing, and the ability to communicate complex technical concepts to non-technical audiences. Familiarity with media data sources, real-time analytics, and stakeholder collaboration is highly valued.

5.5 How long does the Tegna Data Engineer hiring process take?
The typical Tegna Data Engineer hiring process spans 3-5 weeks from application to offer. The timeline can vary depending on candidate availability, scheduling of interviews, and the complexity of the final negotiation stage. Fast-track candidates may complete the process in as little as two to three weeks.

5.6 What types of questions are asked in the Tegna Data Engineer interview?
You can expect a mix of technical and behavioral questions. Technical questions cover ETL pipeline architecture, data warehousing, SQL and Python problem-solving, data quality assurance, and real-world system design challenges. Behavioral interviews focus on teamwork, stakeholder communication, handling ambiguity, and delivering business value through data engineering. Scenario-based and case questions are common, often tailored to Tegna’s media and digital operations.

5.7 Does Tegna give feedback after the Data Engineer interview?
Tegna typically provides high-level feedback through recruiters, especially if you reach the final stages. Detailed technical feedback may be limited due to company policy, but you can expect to receive general insights on your performance and fit for the role.

5.8 What is the acceptance rate for Tegna Data Engineer applicants?
While Tegna does not publish official acceptance rates, the Data Engineer position is competitive, with an estimated acceptance rate of 3-6% for qualified candidates. Demonstrating both technical excellence and strong communication skills will help you stand out.

5.9 Does Tegna hire remote Data Engineer positions?
Yes, Tegna does offer remote opportunities for Data Engineers, particularly for roles supporting digital media and analytics teams. Some positions may require occasional travel to Tegna offices for team collaboration or project kickoffs, but remote and hybrid work options are increasingly common.

• Related guides:
  • Tegna Data Engineer interview questions
  • Data Engineer interview guide
  • Top data engineering interview tips

Ready to ace your Tegna Data Engineer interview? It’s not just about knowing the technical skills—you need to think like a Tegna Data Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Tegna and similar companies.

With resources like the Tegna Data Engineer Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition. Whether you’re preparing for questions on scalable ETL pipeline design, data warehousing, SQL and Python, or stakeholder engagement, you’ll find targeted practice and actionable insights to help you showcase your strengths and stand out in every interview round.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!