To succeed in the Sayari interview process, you must align your preparation with the core competencies our engineering team values most. We look for engineers who are not only technically proficient but also highly practical and thoughtful about system design.

Role-Related Knowledge – You must demonstrate a deep understanding of modern data engineering patterns, specifically around distributed computing with Apache Spark and data orchestration with Airflow. Be ready to discuss the internal mechanics of these tools, rather than just how to write basic queries or tasks.

Problem-Solving & Practical Execution – We evaluate your ability to solve real-world data collection and processing problems. This is heavily tested through a practical take-home project. Your ability to write clean, modular, and resilient code that handles real-world web environments is critical.

Architectural Thinking – For senior and principal roles, you must show that you can design systems for scale, reliability, and cost-efficiency. You should be comfortable discussing schema design, infrastructure management, cloud cost optimization, and database selection.

Collaboration & Communication – The technical review round is highly conversational. We assess how well you articulate your design decisions, receive feedback, and collaborate on solving technical challenges. You should treat this round as a collaborative brainstorming session with future peers.

The interview process at Sayari is designed to be highly practical, transparent, and respectful of your time. The entire process typically moves quickly, often concluding within three weeks from the initial screen to the final decision. We focus on evaluating your actual engineering capabilities through tasks that mirror the day-to-day work you will perform on the job.

The journey begins with an initial recruiter screening designed to align on mutual expectations and assess your high-level technical background. Following this, you will transition into a hands-on technical challenge, which is typically a take-home project. This project is the cornerstone of our evaluation, allowing you to showcase your coding standards and problem-solving skills in a low-pressure environment. The final stage is a collaborative technical panel where you will walk through your solution and discuss system design concepts with the engineering team.

The timeline above outlines the standard progression of our hiring loop. The process is streamlined to avoid unnecessary rounds or repetitive technical trivia, focusing instead on continuous, high-signal conversations. Candidates should use this timeline to pace their preparation, ensuring they allocate dedicated time for the take-home project, which typically requires a few hours of focused development.

Web Scraping & Data Extraction

Data extraction is the first and most critical step in the Sayari data lifecycle. Because target web portals vary wildly in complexity and stability, we look for engineers who can build resilient, self-healing scrapers.

During this evaluation, we look for clean code structure, robust error handling, and polite scraping practices. You should show that you understand how to navigate web pages programmatically, parse unstructured HTML, and transform it into structured formats like JSON.

Be ready to go over:

• Resilience patterns – Implementing exponential backoff, retry logic, and proxy rotation to handle network instability.
• Parsing techniques – Using libraries like Beautiful Soup, Scrapy, or Selenium to navigate complex DOM structures and dynamic Javascript-rendered content.
• Data validation – Implementing schema validation at the ingestion boundary to catch malformed data early.
• Anti-scraping mitigation – How to identify and bypass common rate-limiting and bot-detection mechanisms responsibly.

Example scenarios:

• Designing a scraper that extracts nested corporate tables from a site that dynamically loads content via AJAX.
• Handling a scenario where a target government registry website occasionally returns 502 Bad Gateway errors during high-traffic periods.

Distributed Data Processing (Apache Spark)

Once data is extracted, it must be processed, cleaned, and integrated. At our scale, this requires expert-level mastery of Apache Spark.

We evaluate your depth of knowledge regarding Spark execution plans, memory management, and optimization strategies. We want to see that you can write code that runs efficiently across large clusters without wasting cloud resources.

Be ready to go over:

• Query optimization – Reading and interpreting Spark execution plans to identify bottlenecks, unnecessary shuffles, or inefficient joins.
• Partitioning strategies – Choosing the right partition keys to avoid data skew and ensure balanced cluster utilization.
• Caching and persistence – Deciding when and how to cache intermediate DataFrames to optimize iterative processing workflows.
• Advanced concepts (less common) – Custom Catalyst Optimizer rules, tuning serialization (Kryo), or writing custom Spark accumulation logic.

Example scenarios:

• Optimizing a pipeline where a few massive corporate entities cause severe data skew, grinding your Spark stage progress to a halt.
• Re-architecting a batch Spark job to run incrementally, drastically reducing daily compute costs.

System Architecture & Graph Modeling

To deliver actionable risk intelligence, Sayari connects disparate data points into a global commercial graph. This requires strong architectural foresight and data modeling expertise.

We assess your ability to design scalable, automated, and highly resilient graph build pipelines. This includes selecting the right storage engines, designing flexible schemas, and managing data flows into databases like Elasticsearch, Memgraph, and Cassandra.

Be ready to go over:

• Entity resolution – Designing pipelines that can accurately determine when two differently spelled records refer to the same physical entity.
• Graph schema design – Representing directed relationships, ownership percentages, and historical changes over time.
• Infrastructure as Code (IaC) – Automating the deployment of data pipelines and database schemas using tools like Terraform.

Example scenarios:

• Modeling a complex corporate network where beneficial ownership flows through multiple shell companies across different countries.
• Designing a pipeline that syncs processed graph data from a batch system into a low-latency graph database for real-time querying.