Tegna Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Tegna? The Tegna Business Intelligence interview process typically spans a range of question topics and evaluates skills in areas like data modeling, ETL pipeline design, analytics, stakeholder communication, and presenting actionable insights. Interview preparation is especially important for this role at Tegna, as candidates are expected to demonstrate both technical expertise and the ability to translate complex data findings into clear, business-oriented recommendations that drive decision-making across diverse teams.

In preparing for the interview, you should:

• Understand the core skills necessary for Business Intelligence positions at Tegna.
• Gain insights into Tegna’s Business Intelligence interview structure and process.
• Practice real Tegna Business Intelligence 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 Business Intelligence interview process, along with sample questions and preparation tips tailored to help you succeed.

Tegna is a leading media company specializing in broadcast television, digital media, and marketing services across the United States. Operating over 60 television stations in more than 50 markets, Tegna delivers news, entertainment, and information to millions of viewers daily. The company is committed to journalistic excellence, innovation, and serving local communities. As a Business Intelligence professional at Tegna, you will help transform data into actionable insights, supporting strategic decision-making and enhancing the company’s media operations and audience engagement.

As a Business Intelligence professional at Tegna, you are responsible for gathering, analyzing, and interpreting data to support data-driven decision-making across the organization. You will work closely with various teams, including sales, marketing, and content, to develop dashboards, generate reports, and uncover insights that help optimize business operations and audience engagement. Typical responsibilities include identifying key performance metrics, automating data processes, and presenting actionable findings to stakeholders. This role is essential in enabling Tegna to better understand market trends and viewer behaviors, ultimately supporting the company’s mission to deliver impactful media and information services.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough evaluation of your application materials, focusing on your experience with business intelligence, data warehousing, ETL pipelines, and your ability to communicate data-driven insights to both technical and non-technical stakeholders. Reviewers look for demonstrated expertise in designing scalable data solutions, data modeling, and experience with analytics tools. Tailor your resume to highlight relevant project experience, technical skills (such as SQL, data visualization, and statistical analysis), and your impact on business outcomes.

2.2 Stage 2: Recruiter Screen

Next, a recruiter will reach out for an initial phone screening. This conversation centers on your motivation for joining Tegna, your understanding of the business intelligence function, and your career trajectory. Expect questions about your background, key technical competencies, and your ability to translate complex data into actionable insights. Prepare by articulating your experience in driving business value through analytics, as well as your interest in Tegna’s mission and media landscape.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or more interviews with senior analysts, BI engineers, or data team leads. You may encounter technical case studies, system design scenarios, and practical problem-solving exercises. Expect to discuss or whiteboard topics such as designing a data warehouse, building ETL pipelines, ensuring data quality, and real-time data processing. You may also be asked to analyze A/B test results, address data quality issues, or design dashboards for business stakeholders. To prepare, review your experience with data modeling, analytics experiments, and communicating findings to diverse audiences.

2.4 Stage 4: Behavioral Interview

A behavioral interview will assess your collaboration skills, adaptability, and ability to work with cross-functional teams. Panelists may include business stakeholders and analytics managers. You’ll be asked to describe how you’ve handled project challenges, communicated complex findings to non-technical audiences, or resolved stakeholder misalignment. Use the STAR method to structure your responses, emphasizing teamwork, problem-solving, and the business impact of your work.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of a series of interviews with key decision-makers, such as the BI team manager, analytics director, and representatives from partner business units. This round may include a technical presentation, a live case study, and deeper dives into your previous projects. You’ll be evaluated on your holistic approach to business intelligence, your ability to design end-to-end data solutions, and your communication skills. Prepare to discuss how you’ve led data projects, managed stakeholder expectations, and delivered actionable insights.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive a call from the recruiter or HR representative to discuss the offer details, including compensation, benefits, and start date. This is an opportunity to ask questions, clarify role expectations, and negotiate terms if needed. Be ready to articulate your value and alignment with Tegna’s goals.

2.7 Average Timeline

The typical Tegna Business Intelligence interview process spans 3-5 weeks from application to offer. Fast-track candidates with highly relevant experience may progress in as little as 2-3 weeks, while the standard process allows for about a week between each stage, depending on interviewer availability and scheduling logistics. Technical case study rounds and onsite interviews may require additional preparation and coordination, but clear communication from recruiters helps keep candidates informed throughout.

Next, let’s break down the specific interview questions you may encounter throughout the Tegna Business Intelligence interview process.

3.1 Data Modeling & Warehousing

Business Intelligence roles at Tegna often require strong data modeling and warehousing skills to support scalable analytics solutions. Expect questions on designing robust architectures, managing ETL pipelines, and ensuring data quality across diverse sources.

3.1.1 Design a data warehouse for a new online retailer
Lay out a dimensional or star schema, detail the fact and dimension tables, and explain how you’d handle slowly changing dimensions and scalability. Reference business requirements and justify choices based on anticipated query patterns and reporting needs.

Example answer: "I would start by identifying core business processes, such as sales and inventory, and create fact tables for each. Dimension tables would include products, customers, and time. For scalability, I’d use partitioning and indexing, and address changing attributes with Type 2 SCDs."

3.1.2 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Discuss considerations for multi-region data, currency conversion, localization, and compliance. Address how you’d structure the warehouse to support both global and local reporting.

Example answer: "I’d incorporate region and currency dimensions, ensure data normalization for international standards, and design ETL processes to handle different time zones and regulatory requirements. The architecture would support roll-up and drill-down analytics for both global and local teams."

3.1.3 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Describe how you’d build a robust pipeline to handle schema drift, data validation, and error handling. Emphasize modularity and monitoring.

Example answer: "I’d use a modular ETL framework with schema detection and mapping layers, integrate validation steps, and set up automated alerts for anomalies. The pipeline would log errors and support rapid schema updates with minimal downtime."

3.1.4 Design a system to synchronize two continuously updated, schema-different hotel inventory databases at Agoda.
Explain your approach to schema mapping, conflict resolution, and ensuring eventual consistency. Discuss tools or frameworks for real-time data sync.

Example answer: "I’d use a mapping layer to translate fields between schemas, implement a conflict resolution strategy based on timestamps or business rules, and leverage tools like Kafka or Debezium for change data capture and real-time sync."

3.2 Data Quality & ETL

Ensuring high data quality and managing complex ETL processes are critical for Tegna’s BI teams. Prepare to discuss strategies for profiling, cleaning, and validating large datasets, as well as automating quality checks.

3.2.1 Ensuring data quality within a complex ETL setup
Outline your approach to monitoring, validating, and remediating data issues in multi-source ETL pipelines. Mention automated testing and stakeholder communication.

Example answer: "I’d implement data profiling and automated validation checks at each ETL stage, track data lineage, and set up dashboards to monitor quality metrics. Any discrepancies would trigger alerts and a remediation workflow."

3.2.2 How would you approach improving the quality of airline data?
Discuss profiling for missingness, handling duplicates, and resolving inconsistencies. Reference tools for data cleaning and governance.

Example answer: "I’d start with exploratory data analysis to identify gaps, apply imputation or deduplication techniques, and enforce data validation rules. I’d also collaborate with stakeholders to define quality standards and implement ongoing audits."

3.2.3 Let's say that you're in charge of getting payment data into your internal data warehouse.
Explain how you’d design a reliable ingestion process, handle schema changes, and ensure data integrity. Highlight monitoring and error recovery.

Example answer: "I’d use a combination of batch and streaming ingestion, design schema evolution handling, and set up automated checks for reconciliation. Error logs would be monitored, and failed loads retried automatically."

3.2.4 Redesign batch ingestion to real-time streaming for financial transactions.
Describe the transition from batch to streaming, including architecture changes and benefits for business decision-making.

Example answer: "I’d implement a streaming framework like Apache Kafka, design micro-batch processors for real-time updates, and ensure data consistency with transactional guarantees. This would enable up-to-the-minute reporting and faster business insights."

3.3 Statistical Analysis & Experimentation

Tegna BI analysts are expected to have a solid grasp of statistical methods and experiment design. Be ready to discuss hypothesis testing, A/B testing, and statistical rigor in business decision-making.

3.3.1 What is the difference between the Z and t tests?
Explain the assumptions, use cases, and sample size considerations for each test.

Example answer: "Z-tests are used when the population variance is known and the sample size is large, while t-tests are preferred for smaller samples or unknown variance. Both assess mean differences, but t-tests are more robust for limited data."

3.3.2 You are testing hundreds of hypotheses with many t-tests. What considerations should be made?
Discuss multiple testing corrections, false discovery rate, and statistical significance.

Example answer: "I’d apply corrections like Bonferroni or Benjamini-Hochberg to control for Type I errors, monitor the false discovery rate, and ensure that significant results are not due to chance."

3.3.3 An A/B test is being conducted to determine which version of a payment processing page leads to higher conversion rates. You’re responsible for analyzing the results. How would you set up and analyze this A/B test? Additionally, how would you use bootstrap sampling to calculate the confidence intervals for the test results, ensuring your conclusions are statistically valid?
Describe experimental design, metrics, and statistical methods for inference and confidence intervals.

Example answer: "I’d randomize users, track conversion rates, and use bootstrap resampling to estimate confidence intervals around the lift. I’d check for statistical significance and ensure the experiment is powered to detect meaningful differences."

3.3.4 How would you design a training program to help employees become compliant and effective brand ambassadors on social media?
Explain how you’d measure program effectiveness, design experiments, and track behavioral change.

Example answer: "I’d develop pre- and post-training surveys, monitor social media engagement, and use A/B testing to assess the impact of different training modules on compliance and effectiveness."

3.4 Business Impact & Communication

Business Intelligence at Tegna is about translating data into actionable business insights. You’ll be asked about communicating complex findings, tailoring presentations, and making data accessible to non-technical audiences.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss strategies for audience analysis, visual storytelling, and simplifying technical jargon.

Example answer: "I tailor my presentations by first understanding the audience’s background, using clear visuals and analogies, and focusing on actionable takeaways. I adjust the level of technical detail as needed."

3.4.2 Making data-driven insights actionable for those without technical expertise
Explain your approach to simplifying concepts and connecting insights to business goals.

Example answer: "I use relatable examples, avoid jargon, and link insights to business objectives. I ensure stakeholders understand both the findings and their implications for decision-making."

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Describe how you use dashboards, storytelling, and iterative feedback to improve understanding.

Example answer: "I build interactive dashboards, use color and layout to highlight key metrics, and solicit stakeholder feedback to refine visualizations for clarity and impact."

3.4.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Share your process for expectation management, communication loops, and consensus building.

Example answer: "I schedule regular check-ins, document changes, and use prioritization frameworks to align on goals. I communicate trade-offs and ensure all voices are heard in decision-making."

3.5 Applied Analytics & System Design

Tegna BI analysts often partner with engineering and product teams to design analytical systems and pipelines. Expect questions about system design, process automation, and translating business requirements into technical solutions.

3.5.1 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Describe each pipeline stage: ingestion, cleaning, feature engineering, model training, and serving predictions.

Example answer: "I’d ingest raw data via scheduled ETL jobs, clean and transform it, engineer relevant features, and train predictive models. Results would be served through APIs or dashboards for real-time decision support."

3.5.2 Design a secure and scalable messaging system for a financial institution.
Discuss security, scalability, and compliance considerations in your architecture.

Example answer: "I’d use end-to-end encryption, role-based access controls, and scalable cloud infrastructure. Compliance with financial regulations would be built into data retention and audit logging."

3.5.3 Design and describe key components of a RAG pipeline
Explain retrieval, augmentation, and generation stages, and how you’d monitor accuracy and performance.

Example answer: "I’d implement document retrieval, context augmentation, and response generation. Monitoring would track relevance and accuracy, with feedback loops for continuous improvement."

3.5.4 Model a database for an airline company
Outline key tables, relationships, and normalization strategies for scalable analytics.

Example answer: "I’d model flights, passengers, bookings, and crew as separate tables, ensure referential integrity, and use normalization to avoid redundancy. Indexing would support fast queries for operational reporting."

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
How to answer: Share a specific business problem, your analysis approach, and the impact of your recommendation. Quantify results if possible.

Example answer: "At my previous company, I analyzed customer churn data and identified key drivers. My recommendation led to a targeted retention campaign that reduced churn by 10%."

3.6.2 Describe a challenging data project and how you handled it.
How to answer: Outline the challenge, your problem-solving steps, and the outcome. Emphasize resourcefulness and teamwork.

Example answer: "I managed a project with incomplete sales data, collaborated with IT to recover missing records, and developed a robust imputation strategy that preserved analysis integrity."

3.6.3 How do you handle unclear requirements or ambiguity?
How to answer: Explain your approach to clarifying goals, engaging stakeholders, and iterating on deliverables.

Example answer: "I schedule stakeholder interviews, document requirements, and use prototypes to confirm understanding before full-scale development."

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
How to answer: Describe your communication strategy and how you built consensus.

Example answer: "I presented data supporting my approach, invited feedback, and incorporated their suggestions to reach a solution everyone supported."

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?
How to answer: Discuss prioritization frameworks and transparent communication.

Example answer: "I used MoSCoW prioritization, communicated trade-offs, and secured leadership sign-off to protect project scope and quality."

3.6.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
How to answer: Detail your communication, interim deliverables, and negotiation tactics.

Example answer: "I provided a revised timeline, delivered a prototype to demonstrate progress, and negotiated phased delivery to meet critical milestones."

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
How to answer: Focus on persuasion, relationship-building, and evidence-based arguments.

Example answer: "I built trust by sharing early wins and presented compelling data that aligned with stakeholders’ objectives, leading to adoption of my recommendation."

3.6.8 Walk us through how you handled conflicting KPI definitions (e.g., 'active user') between two teams and arrived at a single source of truth.
How to answer: Describe your process for reconciliation and consensus-building.

Example answer: "I gathered both teams, facilitated a workshop to align on definitions, and documented a standardized KPI framework for future reporting."

3.6.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
How to answer: Highlight your automation strategy and impact on team efficiency.

Example answer: "I developed automated scripts to detect and flag anomalies, reducing manual checks and improving data reliability."

3.6.10 Describe a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
How to answer: Explain your approach to missing data, confidence intervals, and communication of uncertainty.

Example answer: "I profiled missingness, used statistical imputation for key fields, and shaded unreliable sections in visualizations to transparently communicate uncertainty to stakeholders."

4.1 Company-specific tips:

Deepen your understanding of Tegna’s core business as a media company, especially its focus on broadcast television, digital media, and marketing services. Research Tegna’s market presence, recent news, and strategic initiatives, so you can confidently speak to how business intelligence contributes to audience engagement and operational excellence.

Familiarize yourself with the types of data Tegna works with, such as viewership analytics, ad performance, and digital engagement metrics. This will help you contextualize your technical answers and showcase your ability to translate data into insights that matter for a media-driven organization.

Prepare to discuss how you would approach BI challenges unique to the media industry, such as integrating disparate data sources from television, digital platforms, and third-party vendors. Highlight your experience with multi-source data environments and how you ensure data consistency and reliability for business stakeholders.

Demonstrate your awareness of Tegna’s commitment to serving local communities and journalistic excellence. Be ready to articulate how data-driven decision-making can support content strategy, audience targeting, and the company’s broader mission.

4.2 Role-specific tips:

Showcase your expertise in designing scalable data warehouses and robust ETL pipelines. Be ready to explain your approach to schema design, data modeling (star and snowflake schemas), and handling slowly changing dimensions. Reference specific examples where you built or optimized data architectures to support analytics and reporting needs.

Emphasize your strategies for ensuring data quality within complex ETL setups. Discuss how you implement automated validation checks, monitor data lineage, and remediate issues. Share examples of profiling data for missingness, handling duplicates, and collaborating with stakeholders to define and maintain quality standards.

Prepare to discuss your experience with real-time data processing and transitioning from batch to streaming architectures. Articulate the business value of up-to-the-minute analytics in a fast-paced media environment, and describe how you ensure data integrity and scalability in streaming pipelines.

Demonstrate your statistical analysis skills, particularly in A/B testing, hypothesis testing, and experiment design. Illustrate how you use statistical rigor to draw actionable insights and make recommendations that drive business outcomes. Be prepared to walk through examples of analyzing test results and communicating statistical findings to non-technical audiences.

Highlight your ability to present complex data insights with clarity and adaptability. Practice tailoring your communication style to different audiences, using data visualization, storytelling techniques, and clear analogies. Share examples where you successfully made data accessible and actionable for business leaders or cross-functional teams.

Show your experience in resolving misaligned stakeholder expectations and managing ambiguity. Describe your approach to regular communication, consensus-building, and prioritization frameworks that keep projects on track and deliver value.

Lastly, be ready to discuss your process for automating recurrent data-quality checks and building scalable analytical systems. Explain how you translate business requirements into technical solutions, and how you balance speed, accuracy, and flexibility in your BI projects.

5.1 How hard is the Tegna Business Intelligence interview?
The Tegna Business Intelligence interview is challenging but highly rewarding for candidates who prepare thoroughly. You’ll be tested on your ability to design scalable data solutions, manage complex ETL pipelines, and translate intricate data findings into actionable business insights. Expect a mix of technical, case-based, and behavioral questions that assess both your analytical rigor and your communication skills. Candidates with experience in media analytics, data warehousing, and stakeholder management will find themselves well-positioned.

5.2 How many interview rounds does Tegna have for Business Intelligence?
Typically, Tegna’s Business Intelligence interview process consists of five to six rounds. These include an initial recruiter screen, technical/case interviews, a behavioral round, final onsite interviews with key decision-makers, and an offer/negotiation stage. Each round is designed to evaluate a specific set of competencies, from technical expertise to business acumen and collaborative skills.

5.3 Does Tegna ask for take-home assignments for Business Intelligence?
Candidates may be given a take-home assignment, such as a case study or data problem, to assess their approach to real-world BI challenges. These assignments often focus on data modeling, ETL pipeline design, or analytics scenarios relevant to Tegna’s media business. The goal is to evaluate your problem-solving process, technical proficiency, and ability to communicate actionable insights.

5.4 What skills are required for the Tegna Business Intelligence?
Success in Tegna’s Business Intelligence role requires strong skills in data modeling, ETL pipeline design, data warehousing, and analytics. Proficiency in SQL, data visualization, and statistical analysis is essential. You should also excel at stakeholder communication, presenting complex findings clearly, and tailoring recommendations for diverse business teams. Experience with media or digital analytics, real-time data processing, and automation of data-quality checks is highly valued.

5.5 How long does the Tegna Business Intelligence hiring process take?
The typical Tegna Business Intelligence hiring process takes between 3 and 5 weeks from application to offer. Fast-track candidates with highly relevant experience may complete the process in as little as 2-3 weeks, while the standard timeline allows about a week between each stage. The process can vary based on interviewer availability and candidate scheduling.

5.6 What types of questions are asked in the Tegna Business Intelligence interview?
Expect a broad spectrum of questions, including technical case studies (data warehouse design, ETL pipelines), data quality and validation scenarios, statistical analysis and experiment design, and behavioral questions focused on stakeholder management and communication. You may be asked to analyze A/B test results, present insights to non-technical audiences, and resolve misaligned expectations between teams.

5.7 Does Tegna give feedback after the Business Intelligence interview?
Tegna generally provides feedback via recruiters after interviews. While detailed technical feedback may be limited, you’ll typically receive high-level insights into your performance and areas for improvement. This feedback can help guide your preparation for future opportunities, whether at Tegna or elsewhere.

5.8 What is the acceptance rate for Tegna Business Intelligence applicants?
While Tegna does not publish exact acceptance rates, the Business Intelligence role is competitive, with an estimated acceptance rate of around 3-6% for qualified applicants. Strong technical skills, relevant media analytics experience, and clear communication abilities help candidates stand out.

5.9 Does Tegna hire remote Business Intelligence positions?
Yes, Tegna does offer remote positions for Business Intelligence roles, depending on team needs and business priorities. Some roles may require occasional travel to headquarters or local stations for collaboration, but remote and hybrid work arrangements are increasingly common within Tegna’s analytics and technology teams.

• Related guides:
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  • Business Intelligence interview guide
  • Top Business Intelligence interview tips
  • Business Intelligence case studies
  • How to Prepare for Business Intelligence Interviews: Success Story

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