At The Lasalle Network, data is the operational backbone that drives efficient staffing, recruiting, and workforce solutions. As a Data Engineer, your role is critical in building, optimizing, and maintaining the data pipelines that power candidate matching algorithms, client market analytics, and internal business intelligence tools. You will ensure that massive volumes of recruitment, financial, and operational data are seamlessly integrated and structured for downstream analysis.

Your work directly impacts the speed and quality of placements, enabling business leaders and recruiters to make data-backed decisions. By transforming raw, unstructured data from applicant tracking systems (ATS), customer relationship management (CRM) platforms, and external market sources into clean, reliable data models, you help shape the strategic direction of the firm. This is an exciting opportunity to work on high-impact projects where your engineering decisions directly influence business efficiency and revenue growth.

To succeed in this role, you must possess a strong blend of technical expertise and practical problem-solving skills. The team values engineers who not only write clean, efficient code but also understand the business context of the data they handle. You will be expected to design robust architectures that can scale with the company's expanding footprint, ensuring high availability and data integrity across all platforms.

To help you prepare effectively, we have compiled representative questions based on real interview experiences for the Data Engineer position at The Lasalle Network. These questions are grouped by category to highlight key patterns and technical expectations.

SQL and Database Querying

This category tests your ability to manipulate data, write efficient queries, and demonstrate a strong understanding of relational database concepts.

• Write a SQL query to find the second-highest salary from an employee table, handling potential null values.
• Explain the difference between a inner join, left join, and full outer join, and describe a scenario where you would use each.

Preparing for an interview at The Lasalle Network requires a balanced approach that covers technical mastery, systemic thinking, and professional communication. You should approach your preparation with a clear understanding of the core criteria the hiring team uses to evaluate candidates.

Role-Related Knowledge – You must demonstrate a deep understanding of core data engineering tools and methodologies. This includes writing optimized SQL queries, scripting robust ETL processes in Python, and understanding how data models align with business requirements. Be ready to explain the trade-offs of different database designs and technologies.

Problem-Solving Ability – Interviewers want to see how you approach complex, ambiguous data challenges. When presented with a technical problem or a system design scenario, break down your thought process out loud. Focus on scalability, data integrity, and edge cases, showing that you can design solutions that are resilient to failure.

Communication & Collaboration – Data engineers do not work in a vacuum; you will constantly interact with business analysts, recruiters, and executive stakeholders. You must be able to translate complex technical concepts into clear, actionable business insights. Practice explaining your past projects simply, focusing on the business value you delivered.

Resilience & Adaptability – The recruitment process can sometimes involve unexpected scheduling shifts or rigorous technical panels. Demonstrating patience, professional follow-through, and a positive attitude during times of ambiguity is highly valued and sets a strong cultural baseline.

The interview process for a Data Engineer at The Lasalle Network is designed to evaluate both your technical execution and your alignment with the team's operational needs. While the exact sequence can vary depending on the hiring team and location, the process generally transitions from a high-level screening to intensive technical and managerial evaluations.

The process often starts with an initial phone screen or a direct technical call. This stage covers basic qualifications, salary expectations, contract details, and a high-level overview of your technical stack. Following this, you will progress to technical interviews that dive deep into SQL, Python scripting, and data structures. The final stage typically involves a managerial interview focusing on your system design capabilities, project management skills, and cultural fit.

Candidates should be prepared for potential administrative delays during the scheduling phases. Proactive communication and structured follow-ups are highly recommended to keep your candidacy moving smoothly through the pipeline.

The visual timeline above outlines the standard progression of the interview stages. Candidates should use this timeline to pace their preparation, ensuring they focus heavily on SQL and Python fundamentals early on before transitioning to system architecture and behavioral scenarios. Please note that the exact duration between stages can fluctuate, requiring patience and proactive communication with your recruiter.

To excel in the technical stages, you must understand the specific competencies being evaluated and how to demonstrate your proficiency in each area.

SQL & Data Manipulation

SQL is a primary tool for data extraction and transformation at The Lasalle Network. Interviewers will evaluate your ability to write clean, performance-oriented queries against large datasets.

Be ready to go over:

• Complex Joins and Aggregations – Understanding how to combine multiple tables efficiently without creating duplicate rows or performance bottlenecks.
• Window Functions – Utilizing functions like ROW_NUMBER(), RANK(), and SUM() OVER() to perform analytical calculations.
• Query Optimization – Identifying slow queries, understanding execution plans, and implementing indexing strategies.
• Advanced concepts (less common) – Recursive common table expressions (CTEs), partitioning strategies, and database transaction isolation levels.

Example questions or scenarios:

• "Write a query to identify active candidates who have not been submitted to a job opening in the last 30 days."
• "How would you rewrite a subquery into a join to improve execution time on a table with millions of rows?"

Python & Algorithmic Problem Solving

Python is used extensively for building ETL pipelines and scripting automation tasks. You will be tested on your coding efficiency and your ability to apply appropriate data structures.

Be ready to go over:

• Data Structures – Efficient use of lists, dictionaries, sets, and tuples to manipulate data in memory.
• Data Ingestion Scripting – Writing scripts to read from APIs, parse JSON/XML payloads, and handle data type casting.
• Error and Exception Handling – Implementing robust try-except blocks, retries, and detailed logging to prevent silent pipeline failures.
• Advanced concepts (less common) – Generator functions for memory-efficient data streaming and parallel processing using multiprocessing libraries.

Example questions or scenarios:

• "Write a Python function that takes a list of dictionary objects representing candidates and groups them by their primary skill set."
• "How do you handle rate-limiting and connection timeouts when pulling data from an external ATS API?"

Data Pipeline Architecture & Design

This area evaluates your ability to design scalable, reliable, and maintainable data integration systems.

Be ready to go over:

• ETL vs. ELT Pipelines – Knowing when to transform data on-the-fly versus loading raw data directly into a modern cloud data warehouse.
• Data Quality and Schema Evolution – Implementing checks to detect missing values, anomalies, and changes in source schemas.
• Data Modeling – Designing star schemas, snowflake schemas, and understanding dimensional modeling concepts.
• Advanced concepts (less common) – Designing event-driven pipelines, implementing change data capture (CDC), and managing metadata.

Example questions or scenarios:

• "Walk us through how you would design a pipeline to ingest daily financial reports from an external vendor, ensuring no duplicate records are processed."
• "What strategy would you use to handle late-arriving data in a daily reporting pipeline?"