At Inc., data is the foundation of every strategic decision, product feature, and operational optimization. As a Data Engineer, you will be responsible for designing, building, and maintaining the scalable data pipelines and cloud infrastructure that power our global operations. This role is highly cross-functional, sits at the intersection of software engineering and data analytics, and directly impacts our ability to deliver real-time insights to business leaders, product managers, and data scientists.

You will work with modern data architectures, transitioning legacy systems into robust, cloud-native environments using tools like Snowflake, Databricks, and Azure. Whether you are optimizing complex ELT/ETL pipelines, implementing advanced data quality frameworks, or managing massive data warehouses, your work ensures that our data systems are highly available, cost-efficient, and secure. At Inc., you are not just managing databases; you are building the core data products that drive our next generation of growth.

This position demands a strong engineering mindset, deep technical expertise in distributed computing, and the ability to solve ambiguous data challenges. You will collaborate with engineering, product, and business teams to understand their requirements and translate them into efficient data models. If you thrive in a fast-paced environment and enjoy solving complex scaling problems, the Data Engineer role at Inc. offers an exceptional opportunity to make a measurable impact at scale.

The questions you will encounter during the Inc. interview process are designed to test your core engineering capabilities, analytical thinking, and system design expertise. These questions are compiled from real candidate experiences and reflect the actual scenarios you will face. While the exact questions may vary depending on the team and seniority level, they consistently follow key patterns focused on data manipulation, system performance, and architectural design.

SQL & Data Manipulation

These questions evaluate your ability to write clean, optimized SQL queries to manipulate complex datasets and solve business-logic problems.

• Write a query to find the top three highest-earning employees in each department using window functions.
• Given a table of user logins, write a query using Common Table Expressions (CTEs) and date filtering to identify daily active users who logged in three consecutive days.

Preparing for an interview at Inc. requires a balanced approach that covers core computer science fundamentals, deep database expertise, and system design principles. You should treat each interview round as an opportunity to demonstrate your engineering rigor and your ability to collaborate with others.

Technical Excellence – You must demonstrate a deep understanding of database internals, SQL optimization, and distributed systems. Interviewers will closely evaluate your ability to write clean, maintainable code in Python or Scala, as well as your familiarity with cloud infrastructure.

Analytical Problem-Solving – When presented with coding or architectural challenges, do not jump straight to the solution. Clearly explain your thought process, discuss trade-offs between different approaches, and state any assumptions you are making before writing a single line of code.

Ownership and Impact – At Inc., we value engineers who take complete ownership of their systems. Be prepared to discuss your past projects in detail, focusing on the business impact of your work, the technical challenges you overcame, and how you collaborated with cross-functional partners.

Cultural Alignment – We look for team members who are collaborative, open to feedback, and comfortable navigating ambiguity. Use your behavioral interviews to highlight how you handle constructive criticism, resolve technical disagreements, and mentor junior engineers.

The interview process at Inc. is rigorous and comprehensive, designed to evaluate both your technical depth and your cultural alignment with our engineering teams. The process typically begins with an HR screening, followed by an online assessment and multiple rounds of technical and behavioral discussions. The entire process is structured to give you a clear understanding of the role while allowing our team to assess your readiness for our scale.

You can expect the process to move efficiently, but you should prepare for high standards at every stage. The technical rounds are conducted by senior engineers and engineering managers who will dive deep into your practical coding skills, data warehousing knowledge, and system design capabilities.

The timeline above outlines the typical progression of a candidate through the Inc. hiring funnel. While the exact sequence may vary slightly depending on the specific team or location, most candidates complete the process within three to four weeks. Use this timeline to pace your preparation, ensuring you allocate sufficient time for coding practice, SQL review, and system design concepts before your onsite interviews.

SQL and Data Warehousing

This area is the backbone of the Data Engineer role at Inc.. You will be evaluated on your ability to write highly optimized queries and design clean, scalable data models. Interviewers want to see that you understand how databases execute queries under the hood and how to structure data for maximum performance.

Be ready to go over:

• Window Functions & CTEs – Writing complex analytical queries to rank data, calculate running totals, and isolate specific subsets of data.
• Data Modeling – Designing Star and Snowflake schemas, understanding dimension tables versus fact tables, and implementing slowly changing dimensions (SCDs).
• Query Optimization – Analyzing execution plans, identifying bottlenecks, and optimizing joins, indexes, and partitions in platforms like Snowflake or Redshift.
• Advanced concepts (less common) – Materialized views, clustering keys, micro-partitioning, and columnar storage optimization.

Example questions or scenarios:

• "Given a massive table of web traffic logs, write an optimized query to calculate the session duration for each user without causing memory issues."
• "How would you design a schema to track historical changes in customer address data over a ten-year period?"
• "Walk through how you would diagnose and resolve a slow-running query that involves a join between a 1-billion-row table and a 10-million-row table."

Python and Distributed Computing (PySpark / Scala)

You will face coding challenges that test your software engineering fundamentals and your ability to process data at scale. At Inc., we process terabytes of data daily, so understanding distributed computing principles is non-negotiable.

Be ready to go over:

• Data Structures & Algorithms – Mastering arrays, strings, hash maps, and basic search/sort algorithms.
• Spark Architecture – Understanding drivers, executors, tasks, partitions, and how Spark manages memory.
• Lazy Evaluation & Execution Plans – Explaining how Spark optimizes execution graphs and when transformations are actually executed.
• Advanced concepts (less common) – Custom User Defined Functions (UDFs) optimization, broadcast joins, and managing skew in distributed datasets.

Example questions or scenarios:

• "Write a Python function to find the first non-repeating character in a stream of strings."
• "Explain what happens behind the scenes during a wide transformation in Spark, and how you would handle data skew in a large join operation."
• "Implement a PySpark script that reads a directory of CSV files, filters out corrupt records, and writes the output back in partitioned Parquet format."

System Design and Scalability

System design interviews at Inc. focus on your ability to build end-to-end data architectures that are reliable, fault-tolerant, and cost-effective. You will be asked to design systems that handle data ingestion, scheduling, storage, and consumption.

Be ready to go over:

• Orchestration & Schedulers – Designing robust workflows using tools like Airflow or Prefect, managing task dependencies, and handling failures.
• Ingestion & Streaming – Building pipelines to ingest both batch and real-time streaming data using technologies like Kafka or Event Hubs.
• Storage & Caching – Selecting the right storage tiers (S3, ADLS, Snowflake) and implementing caching mechanisms to speed up downstream queries.
• Advanced concepts (less common) – Designing multi-tenant data platforms, implementing data mesh principles, and managing cloud cost optimization.

Example questions or scenarios:

• "Design a centralized data quality monitoring system that alerts engineering teams when incoming data fails schema validation."
• "How would you architect a pipeline to ingest and process 10,000 events per second from mobile applications with a maximum latency of 5 seconds?"
• "Design a scalable workflow scheduler from scratch, explaining how you would handle task state, retries, and worker node failures."

As a Senior Data Engineer or Data Operations Engineer at Inc., your day-to-day work is dynamic, highly technical, and deeply collaborative. You are responsible for keeping our data engine running smoothly while continuously modernizing our stack.

Your primary responsibilities include:

• Pipeline Development – Designing, building, and maintaining robust ELT/ETL pipelines that ingest data from various internal and external sources into our cloud data warehouse.
• Platform Management – Monitoring and supporting our enterprise data platforms, including Snowflake, Databricks, and Azure, to ensure high availability, data integrity, and optimal performance.
• Performance Tuning – Analyzing data workflows, identifying bottlenecks, and implementing optimizations to reduce execution times and cloud compute costs.
• Collaboration & Support – Partnering with data scientists, business analysts, and product managers to understand their data requirements, build custom datasets, and enable self-service analytics.
• Data Governance – Ensuring all data pipelines adhere to strict security, compliance, and quality standards by implementing automated testing, data profiling, and CI/CD workflows.