Forsyth Barnes Data Engineer interview questions & guide 2026

Preparing for a Senior Data Engineer or Lead Data Engineer role through Forsyth Barnes requires a balanced focus on deep technical execution and high-level architectural design. Because these roles are senior-level contract positions, interviewers expect you to hit the ground running with minimal onboarding. Your preparation should focus on demonstrating immediate value, deep domain expertise, and a pragmatic approach to delivery.

To succeed, you must demonstrate mastery across several core evaluation criteria:

Distributed Systems Expertise – You must show a granular understanding of how Spark processes data under the hood. Be ready to discuss execution plans, DAGs, partition management, and driver/executor memory allocation.

Cloud Security and Governance – In the financial crime space, security is not an afterthought. You must prove you can design secure-by-default pipelines on AWS, using encryption, fine-grained access control, and comprehensive lineage tracking.

Pragmatic Technical Leadership – As a lead, you are expected to drive engineering excellence. You should be able to articulate how you design reusable frameworks, establish automated CI/CD pipelines, and mentor others to write clean, testable, and cost-effective code.

The interview process managed by Forsyth Barnes is designed to be highly efficient, rigorous, and transparent, reflecting the fast-paced nature of contract recruitment. Because these contract positions are Outside IR35 and highly compensated, the evaluation focuses heavily on practical capability, architectural decision-making, and immediate cultural fit.

The process typically begins with an initial technical and experience alignment screen with a Forsyth Barnes talent consultant. This is followed by a comprehensive technical interview with the client's engineering leadership team, focusing on live system design, architectural scenarios, and deep-dive questions on Databricks, AWS, and PySpark. The final stage is a leadership and delivery-focused discussion, where you will discuss how you manage stakeholders, drive engineering standards, and execute projects under tight deadlines.

The visual timeline above outlines the typical progression of the interview stages. Candidates should use this timeline to structure their preparation, dedicating the early stages to refining their architectural and technical talking points, and the later stages to preparing for delivery, leadership, and operational scenarios.

To pass the rigorous technical bar, you must demonstrate deep, specialized knowledge in several key architectural and engineering domains.

PySpark & Databricks Performance Optimization

This area evaluates your ability to build highly efficient, cost-optimized, and scalable data pipelines. Interviewers want to know that you write code optimized for distributed environments, avoiding common anti-patterns that lead to bloated cloud bills or pipeline failures.

Be ready to go over:

• Shuffle Minimization – Strategies to reduce data movement across the network, such as broadcast joins, bucketing, and smart partitioning.
• Memory Management – How to configure executor memory, handle disk spilling, and resolve driver OOM issues.
• Delta Lake Internals – Leveraging transaction logs, optimizing file sizes (compaction/OPTIMIZE), and implementing efficient indexing strategies.
• Advanced concepts (less common) – Custom Spark listeners, writing user-defined functions (UDFs) efficiently in Scala/Java vs. Python vectorization, and managing cluster auto-scaling policies for cost efficiency.

Example questions or scenarios:

• "Your nightly pipeline runtime has doubled over the last month, but the data volume only increased by 10%. Walk us through your step-by-step diagnostic process in the Databricks UI."
• "How would you optimize a stateful streaming pipeline in Spark Structured Streaming to prevent state store size explosion?"

Secure AWS Data Ingestion & Orchestration

This domain focuses on your ability to build secure, reliable, and automated ingestion pathways into the lakehouse from diverse, external financial data sources.

Be ready to go over:

• Ingestion Frameworks – Designing robust pipelines using Apache NiFi, secure APIs, and SFTP/FTPS protocols.
• AWS Infrastructure Security – Implementing IAM roles, KMS keys for envelope encryption, and configuring VPC endpoints for secure data transfer.
• Orchestration & Workflow Management – Setting up complex, dependency-driven DAGs in Airflow or Databricks Workflows with built-in alerting and auto-retry logic.
• Advanced concepts (less common) – Integrating AWS Lake Formation with third-party federated query engines, and designing real-time, event-driven ingestion using AWS Lambda and Kinesis.

Example questions or scenarios:

• "Design a secure ingestion pipeline that pulls daily transaction logs from an external partner's SFTP server, decrypts them, and loads them into an S3 Bronze bucket with strict access controls."
• "How do you handle a scenario where an upstream API source is rate-limiting your ingestion pipeline during peak hours?"

Lakehouse Architecture & Medallion Design

You must demonstrate your ability to design clean, governed, and compliant data models that serve as a single source of truth for downstream compliance and ML teams.

Be ready to go over:

• Medallion Layering – Defining clear boundaries and schemas for Bronze (raw ingestion), Silver (cleaned, deduplicated, conformed), and Gold (aggregated, business-level reporting) layers.
• Data Quality & Observability – Implementing automated schema validation, expectations (e.g., Great Expectations or Delta Live Tables expectations), and data reconciliation.
• Lineage & Governance – Designing system-level tracking of data origin, transformations, and destinations for regulatory auditing.
• Advanced concepts (less common) – Implementing slowly changing dimensions (SCD Type 2/Type 4) in a Delta Lake environment, and managing data deletion requests (GDPR/Right to Be Forgotten) across historical cold storage.

Example questions or scenarios:

• "How would you design a data quality framework that automatically quarantines malformed financial transactions at the Silver layer without halting the entire pipeline?"
• "Explain how you would structure metadata-driven pipelines to allow new data sources to be onboarded via configuration files rather than writing new code."

As a Senior Data Engineer or Lead Data Engineer, your day-to-day work will balance hands-on development with high-level architectural design and team alignment. You will be responsible for:

• End-to-End Pipeline Ownership – Designing, building, and maintaining scalable Spark / PySpark pipelines that process both high-throughput batch and real-time streaming financial datasets.
• Architecting the Lakehouse – Implementing and evolving the Databricks medallion architecture, ensuring that data is securely stored, highly performant, and governed according to strict financial regulations.
• Securing Cloud Infrastructure – Managing and configuring secure, production-grade AWS resources, ensuring proper encryption, network isolation, and access control.
• Driving Engineering Best Practices – Leading the adoption of modern CI/CD pipelines using Terraform, automated testing frameworks, and comprehensive logging and observability tools.
• Mentoring and Collaboration – Guiding junior and mid-level engineers on distributed systems design, while collaborating closely with data science, product, and compliance teams to deliver robust data solutions.

To be highly competitive for these positions through Forsyth Barnes, you must possess a strong blend of advanced technical capabilities, architectural experience, and leadership skills.

• Must-Have Technical Skills – Deep expertise in Databricks, PySpark/Spark, Python, SQL, and core AWS services (S3, IAM, KMS, Glue).
• Orchestration & DevOps – Proven hands-on experience with Apache Airflow (or similar orchestration tools) and Infrastructure as Code using Terraform.
• Industry Experience – Strong track record of building large-scale, production-grade data pipelines, ideally within highly regulated sectors such as financial services, fintech, or fraud prevention.
• Leadership & Delivery – Experience mentoring engineers, setting technical standards, and working independently to deliver complex data platforms on time.
• Nice-to-Have Skills – Experience with Apache NiFi, AWS Lake Formation, Delta Live Tables, and knowledge of financial crime compliance and transaction monitoring systems.

Q: What is the IR35 status and rate structure for these contracts? These positions are offered on a contract basis, determined to be Outside IR35. The day rates typically range from £500 to £550 per day, depending on your specific seniority and leadership experience.

Q: What is the hybrid working policy for these London-based roles? The roles require you to be onsite in the London office 2 days per week, with the remaining 3 days working remotely. This hybrid model is designed to facilitate close collaboration with key stakeholders and engineering teams while maintaining flexibility.

Q: How fast does the interview process move? Because these are contract roles with immediate start dates, the process is highly streamlined. You can expect the entire process—from the initial screen to a final decision—to take between 5 to 10 business days, provided scheduling runs smoothly.

Q: Will there be a live coding assessment during the technical round? Yes, you should expect a practical technical assessment. This typically focuses on writing efficient PySpark or Python code to manipulate datasets, solve algorithmic data-processing challenges, or design an architectural solution on a whiteboard.

Q: What distinguishes a "Lead" candidate from a "Senior" candidate in this process? While both roles require exceptional hands-on technical skills, a Lead Data Engineer must demonstrate a proven ability to define architectural standards, manage external stakeholder relationships, design reusable software frameworks, and actively mentor other engineers on the team.

To truly set yourself apart during the interview process, keep these practical, insider tips in mind:

• Quantify Your Achievements: When describing past projects, don't just list the technologies you used. Explain the scale and business impact of your work. For example, instead of saying "I optimized Spark pipelines," say "I refactored a PySpark pipeline processing 10TB of daily transaction data, reducing execution time by 40% and cloud compute costs by £15,000 per month."

• Speak the Language of the Business: Remember that the ultimate goal of this data platform is to stop financial crime. Show that you understand how your data pipelines impact the downstream fraud investigators and machine learning models. Connect your technical decisions (like latency and data quality) directly to business outcomes (like reducing false-positive rates in fraud detection).

• Demonstrate a Pragmatic Delivery Mindset: As a contractor, you are hired to deliver results quickly. During your behavioral and architectural discussions, emphasize how you balance technical excellence with strict project timelines. Show that you know how to build MVP pipelines that are secure and functional, with a clear roadmap for future optimization.

Securing a Senior Data Engineer or Lead Data Engineer contract role through Forsyth Barnes is an exceptional opportunity to work at the absolute forefront of cloud data engineering and financial security. By owning the design and optimization of a modern Databricks + AWS lakehouse, you will play a direct role in safeguarding financial systems from sophisticated fraud and financial crime.

To succeed in this highly competitive selection process, focus your preparation on demonstrating deep expertise in distributed compute optimization, secure cloud architecture design, and pragmatic technical leadership. Be prepared to showcase your ability to write clean, production-grade code while keeping a sharp eye on security, cost efficiency, and delivery timelines.

The compensation data highlights the highly lucrative nature of senior data engineering contracts within the London market. Given the premium day rates and the Outside IR35 status, candidates are expected to demonstrate immediate technical autonomy and a proven track record of delivering enterprise-grade data platforms.

With targeted preparation, a strong focus on practical system design, and a clear articulation of your leadership capabilities, you are well-positioned to stand out in this process. For additional deep-dive resources, practice questions, and peer interview insights, explore the comprehensive material available on Dataford. Good luck with your preparation—your next high-impact engineering role is within reach.