StreetLight Data Data Engineer Interview Guide

Getting ready for a Data Engineer interview at StreetLight Data? The StreetLight Data Data Engineer interview process typically spans 4–6 question topics and evaluates skills in areas like data pipeline design, ETL systems, data cleaning and organization, scalable architecture, and communicating technical insights to diverse audiences. Interview preparation is especially important for this role at StreetLight Data, as candidates are expected to demonstrate their ability to transform raw mobility data into actionable insights, optimize cloud-based and big data solutions, and collaborate effectively with cross-functional teams in a fast-paced, analytics-driven environment.

In preparing for the interview, you should:

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

StreetLight Data is a leading provider of mobility analytics, transforming how transportation and urban planning decisions are made through the innovative use of Big Data. By leveraging proprietary machine-learning algorithms and data from mobile devices, navigation systems, and IoT sources, StreetLight delivers actionable insights into travel patterns via its SaaS platform, StreetLight InSight®. Supporting over 10,000 projects monthly for both government and private sector clients, the company empowers more efficient infrastructure development and adaptation to emerging mobility trends. As a Data Engineer, you will play a pivotal role in building scalable data pipelines and productizing analytics that drive smarter, data-driven transportation solutions.

As a Data Engineer at StreetLight Data, you will design, build, and maintain robust data pipelines that process large-scale mobility and location datasets for the StreetLight InSight® analytics platform. You will collaborate closely with data scientists, product managers, and engineers to transform raw data into actionable, organized formats, optimize system performance, and productize advanced data science algorithms. Responsibilities include solving data integration challenges, ensuring scalability and efficiency of software components, and supporting the development of innovative analytics solutions for transportation projects. This role is key to enabling high-quality, on-demand mobility insights that support infrastructure planning and transportation analysis.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your resume and application materials by the data engineering hiring team. They assess your experience building and maintaining scalable data pipelines, expertise with Python and other programming languages, and familiarity with SQL and relational databases. Demonstrating hands-on experience with cloud and big data technologies, as well as any exposure to geospatial or transportation data, will help your profile stand out. To prepare, ensure your resume clearly highlights relevant technical projects, data pipeline work, and collaboration with cross-functional teams.

2.2 Stage 2: Recruiter Screen

Next, a recruiter will reach out for a 30-minute phone screen to discuss your background, motivations for applying, and alignment with StreetLight Data’s mission. Expect questions about your previous data engineering roles, your approach to problem-solving, and your ability to communicate technical concepts to both technical and non-technical stakeholders. Preparation should focus on articulating your experience with data engineering tools, your understanding of the transportation analytics domain, and your ability to work in distributed, collaborative environments.

2.3 Stage 3: Technical/Case/Skills Round

The technical round is typically conducted by senior data engineers or the Director of Data Science and involves a mix of live coding, system design, and case-based problem-solving. You may be asked to design and optimize data pipelines (e.g., for large-scale mobility or sensor data), address data cleaning and integration challenges, or discuss your approach to building robust ETL systems. Familiarity with cloud data platforms, SQL optimization, and algorithmic thinking will be tested. Prepare by reviewing end-to-end pipeline design, troubleshooting data quality issues, and demonstrating how you would scale solutions for real-world analytics platforms.

2.4 Stage 4: Behavioral Interview

In this stage, you’ll meet with data team members, product managers, and potentially cross-functional stakeholders. The focus is on your ability to communicate complex data insights, collaborate with diverse teams, and adapt your technical explanations for different audiences. You’ll be expected to share examples of navigating project hurdles, balancing technical trade-offs, and making data-driven decisions under tight deadlines. To prepare, reflect on past experiences where you have demystified technical topics, facilitated data-driven decision-making, and contributed to a positive team culture.

2.5 Stage 5: Final/Onsite Round

The final round typically consists of multiple interviews, sometimes virtual, with data engineering leadership, product leaders, and future colleagues. You may face deeper technical dives (such as system design for high-throughput data ingestion or productizing machine learning algorithms), scenario-based discussions about scaling data platforms, and collaboration exercises. There may also be a presentation component where you explain a complex data project or insight to a non-technical audience. Prepare by practicing clear, concise communication, and be ready to discuss both technical details and broader business impact.

2.6 Stage 6: Offer & Negotiation

If you successfully progress through the interviews, the recruiter will contact you with an offer. This stage covers compensation, benefits, team fit, and expected start date. You’ll have the opportunity to negotiate terms and clarify any remaining questions about the role or company culture. Preparation involves researching typical compensation for data engineers in your region and reflecting on your priorities for the offer.

2.7 Average Timeline

The StreetLight Data Data Engineer interview process typically spans 3–5 weeks from initial application to offer. Fast-track candidates with highly relevant experience and prompt scheduling may complete the process in as little as 2–3 weeks, while the standard pace allows about a week between each stage to accommodate panel availability and take-home assessment timelines. The process is designed to thoroughly evaluate both technical depth and cross-functional collaboration skills.

Next, let’s dive into the types of interview questions you can expect throughout the StreetLight Data Data Engineer process.

3.1. Data Pipeline Design and Optimization

Expect questions probing your experience architecting scalable, reliable pipelines to support large-scale analytics and data products. Focus on demonstrating your ability to design robust workflows, handle heterogeneous data sources, and optimize for performance and reliability.

3.1.1 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Break down the pipeline stages from ingestion to transformation, storage, and serving. Emphasize automation, scalability, and monitoring, and discuss how you’d handle real-time vs. batch processing.

3.1.2 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Outline your approach to validation, error handling, schema evolution, and performance optimization. Highlight your experience with orchestration tools and cloud storage solutions.

3.1.3 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Discuss your strategy for schema mapping, deduplication, and incremental loads. Include thoughts on monitoring, alerting, and ensuring data consistency across sources.

3.1.4 Redesign batch ingestion to real-time streaming for financial transactions.
Explain how you’d migrate from batch to streaming, including technology choices, latency considerations, and fault tolerance. Mention trade-offs between throughput and consistency.

3.1.5 Design a solution to store and query raw data from Kafka on a daily basis.
Describe your process for ingesting, partitioning, and storing high-volume event data. Discuss querying strategies and how you’d ensure efficient access for analytics.

3.2. Data Quality and Cleaning

These questions assess your ability to identify, diagnose, and resolve data quality issues in complex environments. Highlight your experience with profiling, cleaning, and validating large datasets, as well as communicating quality metrics to stakeholders.

3.2.1 Describing a real-world data cleaning and organization project
Share a detailed example of a messy data cleaning effort, including your approach, tools, and how you validated the results. Emphasize reproducibility and documentation.

3.2.2 How would you approach improving the quality of airline data?
Discuss your data profiling techniques, root cause analysis, and remediation strategies. Focus on setting up automated checks and communicating quality improvements.

3.2.3 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Explain your troubleshooting workflow, including logging, alerting, and rollback mechanisms. Highlight how you’d prevent future failures and communicate with stakeholders.

3.2.4 Ensuring data quality within a complex ETL setup
Describe how you’d monitor pipeline health, reconcile discrepancies, and set up validation rules. Discuss cross-team collaboration and the importance of documentation.

3.2.5 Modifying a billion rows
Detail your approach to efficiently processing large datasets, including batching, indexing, and minimizing downtime. Mention performance testing and rollback planning.

3.3. System and Database Design

These questions evaluate your skills in architecting data systems for scalability, reliability, and ease of use. Focus on schema design, technology selection, and balancing business requirements with technical constraints.

3.3.1 Design a data warehouse for a new online retailer
Describe your approach to schema design, partitioning, and indexing. Discuss how you’d support analytics and reporting, and address scalability concerns.

3.3.2 System design for a digital classroom service.
Explain your choices for data models, storage, and integration with other systems. Highlight considerations for privacy, access control, and performance.

3.3.3 Design the system supporting an application for a parking system.
Outline your architectural decisions, focusing on reliability, scalability, and user experience. Discuss data synchronization and real-time updates.

3.3.4 Designing a pipeline for ingesting media to built-in search within LinkedIn
Describe your indexing strategy, handling of unstructured data, and search optimization techniques. Emphasize scalability and latency.

3.3.5 Designing a database system to store payment APIs
Discuss schema design, API integration, and security considerations. Mention transaction logging and auditability.

3.4. Data Analysis and Visualization

These questions test your ability to extract actionable insights from complex datasets and communicate findings effectively. Demonstrate your proficiency in data exploration, visualization, and tailoring results to different audiences.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss your approach to storytelling with data, using visualization best practices and adapting technical depth to your audience.

3.4.2 Demystifying data for non-technical users through visualization and clear communication
Share techniques for simplifying data, choosing intuitive charts, and using analogies. Emphasize stakeholder engagement.

3.4.3 Making data-driven insights actionable for those without technical expertise
Explain your process for translating analytics into business recommendations. Focus on clarity and impact.

3.4.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Describe your visualization choices, handling of outliers, and annotation strategies. Highlight how you’d enable exploration and interpretation.

3.4.5 What kind of analysis would you conduct to recommend changes to the UI?
Outline your approach to user journey mapping, funnel analysis, and identifying pain points. Discuss how you’d prioritize recommendations.

3.5. Programming, Tooling, and Automation

Expect questions about your proficiency with programming languages, automation, and tooling choices. Highlight your experience balancing flexibility, maintainability, and scalability in production environments.

3.5.1 python-vs-sql
Compare the strengths of Python and SQL for different data engineering tasks, and discuss scenarios where you’d prefer one over the other.

3.5.2 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
List your preferred open-source stack, discuss trade-offs, and explain how you’d ensure reliability and scalability.

3.5.3 Design and describe key components of a RAG pipeline
Explain the architecture, data flow, and integration points. Highlight your experience with orchestration, monitoring, and error handling.

3.5.4 Design a data pipeline for hourly user analytics.
Discuss scheduling, data aggregation, and optimization for latency and throughput. Mention monitoring and alerting strategies.

3.5.5 Let's say that you're in charge of getting payment data into your internal data warehouse.
Describe your approach to ETL, data validation, and error handling. Focus on automation and scalability.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision that impacted a business outcome.
Frame your response around a specific project, highlighting the data analysis process, your recommendation, and the measurable result.

3.6.2 Describe a challenging data project and how you handled it.
Discuss the obstacles you faced, your problem-solving approach, and how you ensured project success.

3.6.3 How do you handle unclear requirements or ambiguity in a data engineering project?
Explain your strategies for clarifying scope, communicating with stakeholders, and iterating on solutions.

3.6.4 Walk us through how you built a quick-and-dirty de-duplication script on an emergency timeline.
Share your approach to prioritizing speed, ensuring accuracy, and documenting the solution for future improvement.

3.6.5 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe your communication style, methods for building consensus, and how you demonstrated the value of your recommendation.

3.6.6 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Highlight your approach to rapid prototyping, gathering feedback, and iterating toward a shared goal.

3.6.7 How have you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow?
Discuss your triage process, prioritizing essential data cleaning and analysis, and communicating uncertainty.

3.6.8 Describe a time you had to deliver an overnight report and still guarantee the numbers were “executive reliable.”
Explain your strategies for rapid validation, leveraging automation, and ensuring stakeholder trust.

3.6.9 Tell us about a time you proactively identified a business opportunity through data.
Share how you spotted the opportunity, validated it with analysis, and drove action with your insights.

3.6.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe your automation solution, its impact on workflow efficiency, and how you measured success.

4.1 Company-specific tips:

Familiarize yourself with StreetLight Data’s mission and its impact on the transportation analytics industry. Understand how StreetLight InSight® leverages mobility data from diverse sources—such as mobile devices, navigation systems, and IoT sensors—to deliver actionable insights for infrastructure planning and urban mobility. Be prepared to discuss how large-scale, real-world data can be transformed into meaningful analytics that drive better transportation decisions.

Research recent projects, partnerships, and case studies published by StreetLight Data. Pay attention to the types of mobility trends they analyze, such as traffic patterns, mode share, and the impact of new infrastructure. This context will help you tailor your technical solutions to the company’s focus areas during interviews.

Demonstrate an understanding of the challenges unique to mobility data, such as privacy concerns, data sparsity, and the need for real-time processing. Highlight any experience you have with geospatial analytics, transportation datasets, or large-scale SaaS platforms, as these are directly relevant to StreetLight Data’s core products.

Show that you are passionate about enabling data-driven decision-making for cities, government agencies, and private sector clients. Articulate how your technical skills can help advance StreetLight Data’s mission of making transportation more efficient, sustainable, and equitable.

4.2 Role-specific tips:

Demonstrate your expertise in designing and optimizing scalable data pipelines.
Be ready to walk through end-to-end pipeline architectures, especially those that handle large, heterogeneous datasets similar to mobility and sensor data. Detail your approach to data ingestion, transformation, validation, storage, and serving, and explain how you ensure reliability, performance, and scalability at each stage.

Highlight your experience with ETL systems and cloud-based data platforms.
Discuss specific tools and frameworks you have used for orchestration, such as Airflow or similar schedulers, and cloud services like AWS, GCP, or Azure. Explain how you have tackled challenges like schema evolution, incremental data loads, and error handling in production environments.

Showcase your ability to clean and organize messy, real-world data.
Prepare examples of past projects where you identified data quality issues, implemented profiling and validation routines, and automated data cleaning processes. Emphasize reproducibility, documentation, and how your work improved downstream analytics or business outcomes.

Demonstrate strong SQL and Python skills, especially as they relate to big data processing and analytics.
Be comfortable discussing when you would use SQL versus Python for different tasks, and how you optimize queries for large datasets. If you have experience with distributed processing frameworks like Spark, mention how you have leveraged them for high-volume data.

Be prepared for system and database design questions tailored to analytics use cases.
Practice explaining your approach to schema design, partitioning, and indexing for data warehouses that support fast, flexible analytics. Discuss how you balance technical trade-offs—such as cost, latency, and scalability—when selecting technologies and designing architectures.

Communicate your ability to collaborate with cross-functional teams and explain technical concepts to non-technical stakeholders.
Prepare stories where you translated complex data problems into clear, actionable insights for product managers, executives, or clients. Highlight your adaptability in tailoring your communication style to different audiences.

Show your approach to troubleshooting and maintaining robust data pipelines.
Outline your workflow for diagnosing failures, implementing logging and alerting, and setting up automated quality checks. Discuss how you proactively prevent issues and ensure the reliability of critical data flows.

Be ready to discuss how you would productize analytics and machine learning algorithms within a data engineering context.
Explain how you work with data scientists to deploy models at scale, monitor their performance, and integrate outputs into production data systems. Highlight your familiarity with best practices for versioning, testing, and maintaining analytical products.

Demonstrate your passion for continuous improvement and automation.
Share examples of how you have automated repetitive tasks, improved workflow efficiency, or implemented monitoring and alerting to catch data issues early. Show that you are proactive in making systems more reliable and scalable.

Reflect on your experience balancing speed and rigor, especially under tight deadlines.
Be ready to discuss how you triage urgent requests, prioritize essential data quality checks, and communicate uncertainty when delivering quick-turnaround analytics or reports. Show that you can deliver results without sacrificing reliability.

5.1 How hard is the StreetLight Data Data Engineer interview?
The StreetLight Data Data Engineer interview is challenging and rewarding, with a strong focus on practical problem-solving for large-scale mobility data. Candidates are expected to demonstrate expertise in designing scalable data pipelines, optimizing ETL systems, and handling real-world data quality issues. The process also emphasizes clear communication and collaboration across technical and non-technical teams. Those with hands-on experience in cloud platforms, big data technologies, and transportation analytics will find the technical questions engaging and the behavioral rounds insightful.

5.2 How many interview rounds does StreetLight Data have for Data Engineer?
Candidates typically go through 5–6 interview rounds: an initial application and resume review, a recruiter phone screen, a technical or case-based round, a behavioral interview, a final onsite or virtual interview (which may include presentations and deeper technical dives), and finally the offer and negotiation stage.

5.3 Does StreetLight Data ask for take-home assignments for Data Engineer?
Yes, StreetLight Data may include a take-home technical assignment as part of the process. This could involve designing a data pipeline, solving an ETL challenge, or addressing a real-world data cleaning problem. The assignment is designed to assess your ability to translate requirements into robust, scalable solutions and communicate your approach clearly.

5.4 What skills are required for the StreetLight Data Data Engineer?
Key skills include designing and optimizing scalable data pipelines, advanced SQL and Python programming, experience with cloud platforms (AWS, GCP, Azure), expertise in ETL systems, and proficiency in data cleaning and quality assurance. Familiarity with big data frameworks, geospatial analytics, and transportation datasets is highly valued. Strong communication skills and the ability to collaborate with cross-functional teams are essential.

5.5 How long does the StreetLight Data Data Engineer hiring process take?
The typical timeline is 3–5 weeks from initial application to offer, with some candidates moving faster depending on availability and scheduling. The process allows time for thorough technical and behavioral evaluation, as well as any take-home assessments.

5.6 What types of questions are asked in the StreetLight Data Data Engineer interview?
Expect a mix of technical and behavioral questions: designing scalable data pipelines, optimizing ETL processes, troubleshooting data quality issues, system and database design, and programming challenges in Python and SQL. You’ll also be asked about collaborating with cross-functional teams, presenting data insights to non-technical audiences, and handling ambiguity or tight deadlines in a fast-paced environment.

5.7 Does StreetLight Data give feedback after the Data Engineer interview?
StreetLight Data generally provides feedback through the recruiter, especially if you reach the later stages of the process. While detailed technical feedback may be limited, you can expect high-level insights into your performance and areas for improvement.

5.8 What is the acceptance rate for StreetLight Data Data Engineer applicants?
StreetLight Data Data Engineer roles are competitive, with an estimated acceptance rate of 3–6% for qualified candidates. The process is designed to identify those with both technical depth and strong collaboration skills.

5.9 Does StreetLight Data hire remote Data Engineer positions?
Yes, StreetLight Data offers remote opportunities for Data Engineers, though some roles may require occasional travel for team meetings or onsite collaboration. Remote work is supported, especially for candidates with strong communication and self-management skills.

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

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