Starry, Inc. Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Starry, Inc.? The Starry Data Engineer interview process typically spans 4–6 question topics and evaluates skills in areas like scalable data pipeline design, ETL development, data warehousing, and communicating technical concepts to non-technical audiences. Interview preparation is essential for this role at Starry, as candidates are expected to demonstrate hands-on experience with building robust data solutions, troubleshooting pipeline failures, and making data accessible and actionable for diverse stakeholders in a fast-evolving technology environment.

In preparing for the interview, you should:

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

Starry, Inc. is a technology company focused on delivering high-speed, affordable internet access through innovative wireless solutions. Operating primarily in urban markets, Starry leverages advanced fixed wireless technology to provide reliable broadband services, aiming to simplify connectivity and improve digital inclusion. The company emphasizes transparency, customer-centric service, and bridging the digital divide. As a Data Engineer, you will contribute to optimizing network performance and enhancing data-driven decision-making, supporting Starry’s mission to revolutionize internet access for underserved communities.

As a Data Engineer at Starry, Inc., you are responsible for designing, building, and maintaining the data infrastructure that supports the company’s wireless internet services and business operations. You develop data pipelines, ensure the integrity and reliability of data sources, and collaborate with analytics, product, and engineering teams to enable data-driven decision-making. Your work includes optimizing data storage, processing large datasets, and automating data workflows to support reporting and analytics initiatives. This role is essential for delivering high-quality, actionable insights that help Starry, Inc. enhance customer experiences and improve network performance.

2.1 Stage 1: Application & Resume Review

The initial step involves a thorough review of your application materials, with particular attention given to your experience in building and optimizing data pipelines, ETL processes, and data warehousing. Recruiters and data team leads look for evidence of proficiency with scalable data infrastructure, strong SQL and Python skills, and experience in ensuring data quality and reliability. To prepare, ensure your resume highlights relevant projects demonstrating end-to-end pipeline design, data cleaning, and working with large datasets.

2.2 Stage 2: Recruiter Screen

This stage is typically a 30-minute phone call with a recruiter or talent acquisition specialist. Expect to discuss your background, motivation for joining Starry, Inc., and alignment with the company’s mission. The recruiter will also screen for core technical skills, communication ability, and your understanding of data engineering fundamentals. Preparation should focus on articulating your experience, why you are interested in Starry, and how your skills match the company’s data needs.

2.3 Stage 3: Technical/Case/Skills Round

Led by a data engineering manager or senior engineer, this stage tests your practical skills through a mix of coding challenges, system design problems, and scenario-based case questions. You may be asked to design scalable ETL pipelines, optimize data processing for billions of rows, or troubleshoot data transformation failures. Expect to demonstrate expertise in SQL, Python, and cloud-based data tools, as well as your approach to data cleaning, aggregation, and warehouse architecture. Preparation should include reviewing your past projects, practicing system design, and being ready to discuss trade-offs in technology choices.

2.4 Stage 4: Behavioral Interview

A cross-functional panel or data team lead will assess your collaboration skills, adaptability, and ability to communicate complex technical concepts to non-technical stakeholders. You’ll be expected to share examples of how you’ve presented data insights to various audiences, navigated project hurdles, and fostered data accessibility across teams. Prepare by reflecting on experiences where you’ve influenced decision-making, addressed challenges in data projects, and worked with diverse teams.

2.5 Stage 5: Final/Onsite Round

The final round often consists of multiple interviews with technical leaders, product managers, and potential future teammates. These sessions dive deeper into your technical expertise, system design thinking, and ability to contribute to Starry’s data infrastructure. You may be asked to whiteboard solutions for real-world data engineering problems, discuss your approach to ensuring data quality, and demonstrate your ability to build robust, scalable pipelines under strict constraints. Preparation should focus on your end-to-end engineering process, communication skills, and readiness to discuss recent industry trends.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, the recruiter will reach out to discuss the offer package, including compensation, benefits, and onboarding timeline. This is your opportunity to clarify role expectations, negotiate terms, and confirm your fit within the data engineering team.

2.7 Average Timeline

The typical interview process for a Data Engineer at Starry, Inc. spans approximately 3-4 weeks from initial application to final offer. Fast-track candidates with highly relevant experience or internal referrals may progress in as little as 2 weeks, while standard timelines allow for a week between each stage to accommodate scheduling and feedback. Onsite rounds are usually scheduled within a week of successful technical interviews, and offer negotiation typically concludes within several days after the final round.

Next, let’s dive into the specific interview questions that you may encounter throughout the Starry, Inc. Data Engineer interview process.

3.1. Data Pipeline Design & ETL

Data pipeline design and ETL are fundamental to the Data Engineer role at Starry, Inc., as you’ll be expected to create scalable, robust systems for ingesting, transforming, and serving large volumes of data. Interviewers will focus on your ability to architect solutions that are reliable, efficient, and maintainable. Be prepared to discuss both high-level design choices and detailed implementation trade-offs.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Explain your approach to handling varied data formats, ensuring schema consistency, and building fault-tolerant, scalable ETL processes. Discuss how you would monitor and recover from failures.

3.1.2 Let's say that you're in charge of getting payment data into your internal data warehouse.
Describe the steps for ingesting, cleaning, and validating payment data, highlighting how you ensure data integrity and low-latency reporting.

3.1.3 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Lay out the ingestion, transformation, storage, and serving layers, and discuss how you would optimize for both real-time and batch predictions.

3.1.4 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Focus on error handling, schema evolution, and automation strategies for recurring uploads.

3.1.5 Design a data pipeline for hourly user analytics.
Describe your approach to aggregating large-scale event data efficiently and ensuring timely delivery of analytics.

3.2. Data Modeling & Warehousing

Data modeling and warehousing are essential for enabling analytics and reporting at scale. Expect questions about how you would structure data storage, optimize query performance, and balance normalization with accessibility.

3.2.1 Design a data warehouse for a new online retailer.
Discuss your approach to schema design, partitioning, and supporting both transactional and analytical workloads.

3.2.2 System design for a digital classroom service.
Explain how you would model complex relationships and ensure the system can scale with diverse user interactions.

3.2.3 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Highlight your knowledge of open-source data stack components and cost-effective architecture choices.

3.2.4 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Walk through your troubleshooting process, monitoring strategies, and how you’d implement long-term fixes.

3.3. Data Quality & Cleaning

Ensuring high data quality is crucial for reliable analytics. These questions test your experience with cleaning, profiling, and reconciling messy or inconsistent datasets, as well as your ability to automate quality checks.

3.3.1 Describing a real-world data cleaning and organization project.
Share a detailed example, outlining your steps for identifying, cleaning, and validating data issues.

3.3.2 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Discuss strategies for normalizing disparate data sources and tools you’d use for efficient transformation.

3.3.3 Ensuring data quality within a complex ETL setup.
Describe how you monitor, detect, and remediate data quality issues, especially in multi-source environments.

3.3.4 How would you approach improving the quality of airline data?
Explain your process for profiling, prioritizing, and resolving data quality problems at scale.

3.3.5 How would you modify a billion rows in a production database efficiently?
Detail your approach to bulk updates, avoiding downtime, and ensuring transactional integrity.

3.4. Communication & Stakeholder Collaboration

Data Engineers at Starry, Inc. are expected to collaborate closely with technical and non-technical stakeholders. These questions assess your ability to present insights, translate technical details, and make data accessible.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience.
Describe your process for tailoring presentations to different audiences and ensuring actionable takeaways.

3.4.2 Making data-driven insights actionable for those without technical expertise.
Explain how you break down complex findings and ensure stakeholders understand the implications.

3.4.3 Demystifying data for non-technical users through visualization and clear communication.
Share examples of how you’ve used dashboards or visual tools to empower business teams.

3.4.4 How would you answer when an Interviewer asks why you applied to their company?
Articulate your motivations for joining Starry, Inc., connecting your skills and interests to the company’s mission and challenges.

3.5. Tooling & Technology Choices

Choosing the right tools and technologies is a key part of a Data Engineer’s role. Expect questions about trade-offs, performance, and integration with existing systems.

3.5.1 python-vs-sql
Discuss scenarios where you’d prefer Python over SQL (or vice versa) for data engineering tasks, emphasizing efficiency and maintainability.

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3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe how you identified a business problem, analyzed relevant data, and communicated your recommendation to drive impact.

3.6.2 Describe a challenging data project and how you handled it.
Share the technical and organizational obstacles you faced, your problem-solving approach, and the outcome.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your process for clarifying goals, iterating with stakeholders, and documenting assumptions to ensure alignment.

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?
Discuss how you facilitated open dialogue, incorporated feedback, and reached consensus.

3.6.5 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Describe your methods for reconciling differences, validating data sources, and building trust across teams.

3.6.6 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Highlight how you identified recurring issues, designed automation, and measured improvements in data reliability.

3.6.7 You’re given a dataset that’s full of duplicates, null values, and inconsistent formatting. The deadline is soon, but leadership wants insights from this data for tomorrow’s decision-making meeting. What do you do?
Explain your triage process, prioritization of critical fixes, and how you communicate data caveats under time pressure.

3.6.8 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Share your strategies for workload management, communication, and ensuring timely delivery without sacrificing quality.

3.6.9 Tell us about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss your approach to handling missing data, the impact on analysis, and how you communicated uncertainty to stakeholders.

3.6.10 Give an example of learning a new tool or methodology on the fly to meet a project deadline.
Describe how you identified the need, ramped up quickly, and applied the new skill to deliver results.

4.1 Company-specific tips:

Immerse yourself in Starry, Inc.'s mission to revolutionize internet access through innovative fixed wireless technology. Familiarize yourself with the company’s focus on bridging the digital divide, delivering affordable high-speed internet to underserved communities, and its core values of transparency and customer-centric service. Be prepared to articulate how your technical expertise as a Data Engineer can directly support Starry’s goals of optimizing network performance and enabling data-driven decision-making for both internal teams and end users.

Research recent developments in wireless broadband, especially Starry’s approach to urban connectivity and simplifying internet access. Understand the challenges Starry faces in scaling its infrastructure, handling diverse data sources from network devices, and supporting real-time analytics to improve customer experiences. Demonstrating awareness of these industry-specific challenges will show your genuine interest and alignment with Starry's business.

Reflect on how your experience aligns with Starry’s emphasis on making data accessible and actionable for diverse stakeholders. Prepare examples of collaborating with cross-functional teams, especially in environments where technical and non-technical audiences need clear, impactful insights. This will help you showcase your ability to bridge the gap between engineering and business needs at Starry.

4.2 Role-specific tips:

4.2.1 Master the design of scalable, fault-tolerant data pipelines for heterogeneous sources.
Practice explaining your approach to building ETL pipelines that ingest, transform, and serve data from varied formats and sources, such as network logs, customer usage data, and external partner feeds. Highlight your strategies for schema consistency, error handling, and monitoring failures to ensure robust, reliable data flows that can scale with Starry’s rapid growth.

4.2.2 Demonstrate expertise in data cleaning and quality assurance at scale.
Prepare to discuss real-world projects where you’ve tackled messy, inconsistent datasets—think duplicates, nulls, and disparate formats. Outline your process for profiling data, prioritizing fixes, and automating quality checks to ensure high-integrity analytics. Be ready to explain how you would triage urgent data issues under time pressure and communicate caveats to leadership.

4.2.3 Show proficiency in data modeling and warehouse architecture for fast, flexible analytics.
Review your experience designing data warehouses and modeling complex relationships to support both transactional and analytical workloads. Be prepared to discuss how you optimize schema design, partitioning, and query performance, especially for large-scale event data and real-time reporting scenarios relevant to Starry’s operations.

4.2.4 Highlight your ability to troubleshoot and optimize recurring pipeline failures.
Think through systematic approaches to diagnosing and resolving repeated transformation failures in nightly or hourly data jobs. Discuss how you monitor pipelines, implement long-term fixes, and communicate technical issues to stakeholders, ensuring minimal disruption to business operations.

4.2.5 Articulate trade-offs in tooling and technology choices for data engineering.
Expect questions about when to use Python versus SQL, or how to select open-source tools under budget constraints. Practice explaining your decision-making process, focusing on efficiency, maintainability, and integration with Starry’s existing stack. Show that you can balance innovation with reliability and cost-effectiveness.

4.2.6 Prepare to communicate complex technical concepts to non-technical audiences.
Develop clear, concise ways to present data engineering solutions and insights to product managers, business analysts, and executives. Share examples of tailoring your communication style, using visualizations, and ensuring actionable takeaways for stakeholders with varying levels of technical expertise.

4.2.7 Showcase your adaptability and collaborative skills in cross-functional settings.
Reflect on experiences where you’ve worked with diverse teams, handled ambiguous requirements, or reconciled conflicting definitions and priorities. Be ready to discuss how you facilitate open dialogue, document assumptions, and build consensus to drive successful data projects within a fast-evolving tech environment like Starry, Inc.

4.2.8 Illustrate your ability to learn new tools and methodologies quickly.
Have stories ready about picking up new technologies or frameworks on the fly to meet tight deadlines. Highlight your resourcefulness, willingness to experiment, and how you apply new skills to deliver results in dynamic situations.

4.2.9 Emphasize your commitment to automating and improving data reliability.
Share examples of how you’ve identified recurring data quality issues, designed automated checks, and measured improvements in reliability. Demonstrate your proactive mindset and focus on building sustainable, scalable solutions that prevent future crises and support Starry’s growth.

4.2.10 Practice articulating your motivation for joining Starry, Inc.
Connect your passion for data engineering and your personal values to Starry’s mission and challenges. Be authentic and specific about why you want to contribute, and how your skills will help advance Starry’s vision of digital inclusion and high-speed connectivity for all.

5.1 “How hard is the Starry, Inc. Data Engineer interview?”
The Starry, Inc. Data Engineer interview is considered moderately challenging, especially for those new to designing scalable data pipelines or working with large, complex datasets. The process is designed to rigorously assess your technical depth in ETL development, data warehousing, and troubleshooting, as well as your ability to communicate technical concepts clearly to non-technical stakeholders. Candidates with strong hands-on experience in building robust data solutions and a proactive mindset will find themselves well-prepared to succeed.

5.2 “How many interview rounds does Starry, Inc. have for Data Engineer?”
Typically, the Starry, Inc. Data Engineer interview process consists of 4–6 rounds. This includes an initial application and resume review, a recruiter screen, one or more technical and case interviews, a behavioral interview, and a final onsite or virtual panel round with technical leaders and potential teammates. Each round is designed to evaluate both your technical expertise and your collaboration skills.

5.3 “Does Starry, Inc. ask for take-home assignments for Data Engineer?”
While not always required, Starry, Inc. may include a take-home assignment or a technical case study in the interview process. These assignments often focus on designing or troubleshooting ETL pipelines, cleaning messy data, or implementing data warehousing solutions. The goal is to assess your practical problem-solving skills and your approach to real-world data engineering challenges relevant to Starry’s business.

5.4 “What skills are required for the Starry, Inc. Data Engineer?”
Starry, Inc. seeks Data Engineers with strong skills in scalable ETL pipeline design, data warehousing, SQL and Python programming, and data quality assurance. Familiarity with cloud-based data tools, experience handling large and heterogeneous datasets, and the ability to automate data workflows are essential. Additionally, strong communication skills and the ability to collaborate with both technical and non-technical stakeholders are highly valued.

5.5 “How long does the Starry, Inc. Data Engineer hiring process take?”
The typical hiring process for a Data Engineer at Starry, Inc. takes about 3–4 weeks from initial application to final offer. Fast-track candidates with highly relevant experience or internal referrals may move through the process in as little as 2 weeks, while standard timelines allow approximately a week between each stage for scheduling and feedback.

5.6 “What types of questions are asked in the Starry, Inc. Data Engineer interview?”
You can expect a blend of technical and behavioral questions. Technical questions focus on designing and optimizing data pipelines, ETL processes, data modeling, warehousing, and troubleshooting data quality issues. You may also be asked to solve coding challenges in SQL or Python and to discuss trade-offs in technology choices. Behavioral questions assess your communication skills, collaboration with cross-functional teams, and ability to make data accessible to non-technical audiences.

5.7 “Does Starry, Inc. give feedback after the Data Engineer interview?”
Starry, Inc. typically provides high-level feedback through recruiters after each interview stage. While detailed technical feedback may be limited due to company policy, you can expect to receive general insights on your performance and next steps in the process.

5.8 “What is the acceptance rate for Starry, Inc. Data Engineer applicants?”
While specific acceptance rates are not publicly disclosed, the Starry, Inc. Data Engineer role is competitive. The company seeks candidates with a demonstrated track record in data engineering and a passion for innovative connectivity solutions, so thorough preparation is key to standing out.

5.9 “Does Starry, Inc. hire remote Data Engineer positions?”
Yes, Starry, Inc. offers remote opportunities for Data Engineers, though some roles may require occasional visits to company offices for team collaboration or project kick-offs. Flexibility in work location is often discussed during the later stages of the interview process.

• Related guides:
  • Starry, Inc. Data Engineer interview questions
  • How to Prepare for a Data Engineer Interview
  • “Top Data Engineer interview tips”

Ready to ace your Starry, Inc. Data Engineer interview? It’s not just about knowing the technical skills—you need to think like a Starry 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 Starry, Inc. and similar companies.

With resources like the Starry, Inc. Data Engineer Interview Guide and our latest case study practice sets, you’ll get access to real interview questions on scalable pipeline design, ETL development, data warehousing, and stakeholder collaboration, along with detailed walkthroughs and coaching support designed to boost both your technical skills and domain intuition.

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!