A Data Engineer at Shift Technology plays a pivotal role in transforming how the global insurance industry detects fraud and automates complex claims decisions. Operating at the intersection of big data, artificial intelligence, and enterprise SaaS, you will design and build the robust data pipelines that ingest, clean, and structure massive, highly sensitive datasets. Your work directly enables the company's proprietary machine learning models to run efficiently and deliver real-time, highly accurate insights to insurance fraud investigators.

The impact of this position is immense. Insurance data is notoriously messy, unstructured, and heterogeneous, coming from hundreds of different insurers worldwide in varying formats. As a Data Engineer, you are responsible for creating scalable, reproducible ingestion frameworks that can normalize this data at scale. Without your pipelines, the advanced AI models developed by the data science teams would lack the clean fuel they need to operate, making your role foundational to the company's core value proposition.

Joining the engineering team means tackling complex data architecture challenges alongside top-tier technical talent. You will work on optimizing data processing systems that handle millions of claims daily, balancing the need for extreme data security with high throughput and low latency. It is a highly rewarding environment for engineers who enjoy solving tangible, real-world problems where mathematical precision and clean code directly translate to catching financial crime.

The interview process is designed to test your practical coding ability, your understanding of data manipulation libraries, and your algorithmic thinking. While questions are tailored to your specific team and seniority level, they consistently focus on your hands-on capability to clean, restructure, and analyze data efficiently. The following questions represent common patterns and topics reported by candidates in real interviews.

SQL & Data Manipulation

• Write a SQL query using window functions to identify overlapping insurance policy coverage periods for a single claimant.
• Using Pandas and NumPy, write a Python function to handle missing values in a dataset and apply a specific mathematical normalization formula to a feature column.
• How would you optimize a slow-running SQL join between a massive claims history table and a smaller, frequently updated policy table?

Preparing for an interview at Shift Technology requires a balanced focus on core software engineering principles, practical data manipulation skills, and collaborative communication. The evaluation process is rigorous, aiming to assess how you handle real-world data challenges rather than just theoretical concepts.

To stand out, you should focus your preparation on the following key evaluation criteria:

Technical Rigor – Interviewers evaluate your ability to write clean, production-grade, and highly optimized code. You must demonstrate deep knowledge of Python, SQL, and core data structures, showing that you prioritize code readability and efficiency.

Mathematical & Data Execution – Unique to the Data Engineer role here, you must be comfortable implementing mathematical formulas programmatically. You should be highly proficient in utilizing vectorization techniques to process structured arrays quickly.

Collaborative Problem-Solving – You will be assessed on how you approach ambiguous technical problems. Interviewers want to see how you structure your thoughts, gather requirements, and communicate your design decisions clearly.

Mission Alignment – Showing a strong curiosity about how data engineering powers fraud detection and decision automation is crucial. Be ready to explain how your technical choices directly impact business outcomes and product reliability.

The interview process for a Data Engineer at Shift Technology is structured to thoroughly evaluate both your technical execution and your alignment with the team's culture. Candidates can expect a multi-stage journey that typically spans three to five weeks, moving from initial screening to deep-technical evaluations, and finally to strategic conversations with senior leadership. The process is designed to be transparent, encouraging, and highly interactive.

The journey begins with an HR screen, followed by an in-depth conversation with the Hiring Manager to discuss your background and technical interests. From there, you will enter the technical evaluation phase, which often includes a timed online coding assessment focusing heavily on data manipulation and mathematical logic, followed by an interactive technical interview with a Tech Lead or future teammates. The process culminates in a final round with the CTO and Hiring Manager to discuss high-level architecture, team collaboration, and your long-term career goals.

This visual timeline illustrates the typical progression of steps from your initial application to the final decision. Candidates should use this timeline to plan their preparation phases, ensuring they dedicate ample time to practice coding and data manipulation before reaching the technical assignment stage. Managing your energy throughout these stages will help you remain sharp for the final strategic discussions.

To succeed in the technical stages, you must understand exactly what the engineering team is looking for in each core competency. The evaluations are practical, meaning you will be asked to write code, solve problems, and explain your architectural choices in real-time.

SQL & Data Manipulation (Pandas & NumPy)

This is one of the most critical technical pillars of the interview. Because insurance data is highly structured yet frequently inconsistent, you must prove that you can manipulate, clean, and transform data efficiently without relying on slow, unoptimized loops.

Be ready to go over:

• Vectorized operations – Writing highly efficient operations in Pandas and NumPy to avoid iterating over rows.
• Advanced SQL – Demonstrating mastery of window functions, complex aggregations, CTEs, and query optimization.
• Mathematical formula translation – Implementing mathematical equations and statistical formulas directly into data processing scripts.
• Advanced concepts (less common) – Memory-efficient chunking of massive datasets, custom NumPy ufuncs, and index optimization techniques.

Example questions or scenarios:

• "You are given a dataset of daily claim submissions. Write a Pandas script to calculate a 7-day rolling average of claim amounts, grouped by insurance provider, without using iterative loops."
• "Optimize a SQL query that identifies duplicate policyholders based on matching partial strings and overlapping coverage dates."

Algorithmic Problem Solving

The ability to solve algorithmic challenges is evaluated to ensure you can write scalable code that performs well under heavy data loads. You will be asked to design algorithms, analyze their performance, and walk through your code step-by-step.

Be ready to go over:

• Time and space complexity – Analyzing your solutions using Big O notation and actively seeking ways to optimize them.
• Core data structures – Knowing when to use hash maps, trees, queues, or graphs to solve specific data-routing problems.
• Fuzzy matching and string manipulation – Implementing logic to match and reconcile records with slight variations.

Example questions or scenarios:

• "Walk through how you would design an algorithm to detect loops or circular patterns in a dataset of financial transactions between multiple accounts."
• "Write a Python function to find the longest common subsequence of claim descriptions to identify potential template-based fraud."

Architectural & Team Fit

The final stages of the interview focus on how you work within an engineering ecosystem. You will discuss how you collaborate with Data Scientists, manage pipeline technical debt, and align your work with the broader business goals of Shift Technology.

Be ready to go over:

• Pipeline scalability – Explaining how you design pipelines that can scale horizontally as data volume grows.
• Cross-functional communication – Describing how you translate technical data constraints to data scientists or product managers.
• Data quality and monitoring – Discussing your approach to building automated alerts and data validation checks.

Example questions or scenarios:

• "How would you design a data pipeline that allows a Data Scientist to seamlessly deploy a new machine learning model without disrupting downstream analytics?"
• "Describe a time you disagreed with a technical decision made by a team lead. How did you present your case and reach a resolution?"

As a Data Engineer at Shift Technology, your daily work will revolve around building the foundational data infrastructure that powers the company's SaaS platform. You will be responsible for the entire lifecycle of data, from initial ingestion to downstream consumption by machine learning models and business intelligence tools.

Your primary day-to-day responsibilities will include:

• Designing, building, and maintaining robust, scalable ETL/ELT pipelines to ingest heterogeneous data from global insurance clients.
• Collaborating closely with Data Scientists to understand their feature engineering needs and operationalize machine learning models in production.
• Implementing strict data quality checks, automated validation scripts, and pipeline monitoring tools to ensure data integrity and reliability.
• Optimizing database queries, data storage formats, and pipeline execution times to minimize latency and cloud infrastructure costs.
• Ensuring all data pipelines and storage solutions comply with strict security standards and global data privacy regulations, such as GDPR.