At Equativ, a Software Engineer plays a pivotal role in building, scaling, and maintaining one of the world's leading independent adtech platforms. The engineering team is responsible for unifying server-side capabilities, supply-side programmatic monetization, and demand-side tools into a seamless, high-performance ecosystem. Because Equativ handles billions of programmatic ad transactions daily, your work directly impacts the efficiency, latency, and reliability of real-time bidding (RTB) engines and ad-serving servers globally.

The problems you will solve as a Software Engineer are highly complex and operate at a massive scale. You will be tasked with optimizing ultra-low latency systems where milliseconds directly translate to business revenue. From parsing high-volume bid requests in real-time to resolving complex data discrepancies across multi-platform integrations, the engineering team ensures that the infrastructure remains robust, highly available, and scalable.

This role is ideal for engineers who thrive on technical ownership, data-driven decision-making, and close collaboration. You will work alongside product managers, data analysts, and site reliability engineers to design clean, maintainable backend services and data pipelines. At Equativ, you are not just writing code; you are architecting the infrastructure that powers the future of independent programmatic advertising.

The questions you will encounter during the Equativ selection process are designed to evaluate your core technical competence, analytical capabilities, and domain familiarity. While the exact questions will vary depending on the team and seniority level, they consistently focus on practical engineering problems rather than abstract academic puzzles.

The following questions are grouped by primary technical categories to help focus your preparation.

Backend Development & Algorithms

This category tests your understanding of core programming paradigms, memory management, and clean code principles, with a particular emphasis on technologies like .NET and C#.

• How does garbage collection work in .NET, and how can you optimize memory allocation in a high-throughput application?

To succeed in the Equativ hiring process, you must demonstrate a balanced combination of deep technical expertise and strong analytical problem-solving skills. Your preparation should focus on showing not just what you can build, but how you think about scale, performance, and data integrity.

Technical Rigor and Domain Knowledge – You must demonstrate a strong command of backend technologies, particularly .NET and C# if applying for Microsoft-stack teams, alongside robust SQL querying capabilities. Be ready to explain the architectural trade-offs of your previous technical decisions and show a solid understanding of how web protocols and high-throughput APIs operate.

Analytical Problem-Solving – Equativ engineers must make sense of complex, high-volume data. You will be evaluated on your ability to perform discrepancy analysis and debug data pipeline issues. Approach these exercises systematically, explaining your hypotheses, how you isolate variables, and how you validate your conclusions using tools like SQL or data analysis frameworks.

System Scale Mindset – Because the platform handles billions of requests, interviewers look for candidates who naturally think about latency, concurrency, memory footprint, and network overhead. When designing solutions or writing code, proactively discuss how your implementation will scale under heavy load.

Collaborative Communication – The engineering culture at Equativ is highly collaborative. You should be able to articulate complex technical concepts clearly to both technical peers and business stakeholders. During interviews, treat your interviewer as a collaborator: ask clarifying questions, walk through your thought process out loud, and remain open to feedback.

The interview process at Equativ is structured to thoroughly evaluate your technical capabilities, problem-solving methodology, and cultural alignment. The company aims for a fluid and transparent candidate experience, though the process is thorough and involves multiple distinct touchpoints with the engineering and leadership teams.

The journey typically begins with an initial conversation with a talent acquisition partner to discuss your background, career aspirations, and alignment with the team's needs. Following this, you will transition into a structured technical evaluation phase. This phase is designed to assess your practical coding and analytical skills through a combination of online assessments, take-home exercises, and live technical interviews.

This visual timeline outlines the typical progression from your initial contact to the final offer stage. Candidates should use this sequence to pace their preparation, ensuring they dedicate sufficient time to practice core coding skills before the technical tests and prepare system design concepts ahead of the onsite rounds. While the exact sequence can vary slightly by location and team, the core focus on practical technical evaluation remains consistent globally.

To excel in the Software Engineer interview loop at Equativ, you must understand the specific competencies being evaluated at each technical stage. The engineering team focuses on three core pillars: AdTech data analysis, high-performance backend development, and database scalability.

AdTech Domain & Data Analysis

This evaluation area focuses on your ability to work with programmatic data structures and resolve the complex data issues that arise in multi-platform advertising ecosystems. You must demonstrate an analytical approach to troubleshooting data flows.

Be ready to go over:

• Bid Request Structures – Understanding the JSON payloads of OpenRTB protocols, including device, user, site, and impression objects.
• Discrepancy Analysis – Investigating and resolving data mismatches (such as impression or revenue discrepancies) between different tracking platforms.
• Data Auditing – Working with raw transaction logs to identify anomalies, dropped requests, or latency bottlenecks in the ad delivery path.

Example scenarios:

• Analyzing an excel discrepancy test dataset to find out why a programmatic partner is reporting 15% fewer impressions than the internal ad server.
• Parsing a complex nested JSON bid request payload to extract and filter specific targeting criteria under strict time constraints.

High-Performance Backend Development (.NET / C#)

This area evaluates your practical coding skills, software design patterns, and your ability to write clean, maintainable, and highly performant backend code.

Be ready to go over:

• Memory Management – Optimizing memory allocation, understanding garbage collection behavior, and avoiding memory leaks in long-running services.
• Asynchronous Programming – Leveraging async/await patterns correctly to handle high-concurrency I/O operations without blocking execution threads.
• Clean Code & Design Patterns – Applying SOLID principles, dependency injection, and appropriate design patterns to build modular and testable codebases.
• Advanced concepts (less common) – Low-level memory optimization techniques, custom serialization protocols, and writing lock-free concurrent data structures.

Example scenarios:

• Refactoring a synchronous, resource-heavy backend method into an optimized asynchronous implementation that handles concurrent network requests gracefully.
• Implementing an efficient caching mechanism to store and retrieve frequently accessed targeting configuration data without degrading system performance.

Database & SQL Performance

Because of the massive volume of transactional and reporting data processed by Equativ, database optimization is a core engineering requirement. This area tests your ability to write efficient queries and design scalable database schemas.

Be ready to go over:

• Query Optimization – Analyzing execution plans, identifying bottlenecks, and optimizing complex SQL joins and aggregations on large tables.
• Indexing Strategies – Designing and maintaining appropriate indexes to speed up read queries without severely impacting write performance.
• Schema Design – Structuring relational databases to support high-throughput transactional logging and efficient analytical reporting.

Example scenarios:

• Writing a SQL query to aggregate hourly ad spend and impression data across multiple dimensions, ensuring the query executes efficiently on a multi-million row table.
• Identifying and resolving a database deadlock scenario in a highly concurrent transactional system.