Be ready to go over:

• Azure Data Factory (ADF) – Designing pipelines, parameterization, triggers, and integrating with other Azure services.
• Databricks & PySpark – Managing Spark clusters, optimizing partition sizes, handling data shuffling, and writing PySpark transformations.
• Azure Data Lake Storage (ADLS) – Structuring raw, bronze, silver, and gold data layers for optimal security and query performance.
• Advanced concepts (less common) – Delta Lake features (ACID transactions, time travel), integration with Azure Synapse Analytics, and CI/CD pipelines for data resources.

Example questions or scenarios:

• "How would you design a pipeline in ADF to load files incrementally from ADLS Gen2 to an Azure SQL Database, handling potential schema drift?"
• "Describe how you would optimize a PySpark job that is currently failing with an Out-Of-Memory (OOM) error due to a massive data skew during a join operation."

As a Data Engineer at Affine, your day-to-day responsibilities will center around building and maintaining the data pipelines that power client analytics. You will be responsible for designing, developing, and deploying scalable data pipelines to ingest, transform, and store large-scale datasets from diverse sources. This includes configuring cloud storage, managing compute resources, and setting up orchestration workflows to ensure reliable data delivery.

You will play a key role in collaborating closely with cross-functional teams, including data scientists, business analysts, and client stakeholders. You will translate complex business requirements into technical data architectures, ensuring that downstream teams have access to clean, well-structured, and highly optimized data for reporting and machine learning models.

Additionally, you will focus on performance optimization and system maintenance. This involves monitoring existing pipelines, debugging performance bottlenecks in database queries or Spark jobs, and ensuring strict adherence to data governance, security, and quality standards across all environments.

To be highly competitive for the Data Engineer position at Affine, you must demonstrate a strong technical foundation combined with practical, real-world project experience.

Technical Skills

• Must-have skills – High proficiency in Python (including OOP, data structures, and scripting) and advanced SQL (including window functions, joins, and performance tuning).
• Must-have skills – Hands-on experience with big data processing frameworks, specifically PySpark and Databricks.
• Must-have skills – Practical experience building ETL/ELT pipelines using cloud orchestration tools, preferably Azure Data Factory (ADF) and Azure Data Lake Storage (ADLS).
• Nice-to-have skills – Experience with other cloud platforms (AWS or GCP), data warehousing tools like Snowflake or Synapse, and containerization using Docker and Kubernetes.

Experience and Soft Skills

• Experience level – Typically 2 to 6 years of experience in data engineering, cloud data warehousing, or a similar backend software development role.
• Soft skills – Strong communication and presentation skills, with the ability to explain technical architectures to non-technical client stakeholders.
• Soft skills – A consultative mindset, characterized by proactive problem-solving, adaptability, and the ability to work effectively in fast-paced, multi-client environments.

Q: How difficult are the technical interviews at Affine? A: The technical interviews are generally rated as average to difficult. While the initial rounds focus heavily on fundamental Python and SQL skills, the subsequent rounds dive deeply into practical cloud architecture, PySpark optimizations, and your ability to solve real-world data pipeline challenges.

Q: What is the typical timeline from the first screen to an offer? A: The timeline generally ranges from 2 to 4 weeks. Because Affine hires both for internal teams and specific client projects, the speed of the process can vary depending on client availability and coordination requirements.

Q: Are there client-facing rounds for all Data Engineer roles? A: Not all, but many. Since Affine is a consulting firm, many engineering roles are tied directly to client engagements. If you are being hired for a client-specific team, expect 1 or 2 technical rounds conducted directly by the client's engineering lead.

Q: What differentiates a successful candidate from an unsuccessful one? A: Successful candidates do not just write working code; they explain the design choices behind their code. They can articulate why they chose a specific data model, how they optimize pipelines for cost and performance, and how they handle real-world data quality issues.

Q: What is the hybrid or remote work policy for this role? A: Affine typically operates on a hybrid model, with key engineering hubs located in Bengaluru. The exact split between remote and in-office work often depends on client security requirements and team agreements.

• Structure Your Project Walkthroughs: When discussing past projects, use the STAR method (Situation, Task, Action, Result). Focus on the volume of data you handled, the specific cloud services you used, and the measurable performance improvements you achieved.
• Brush Up on Software Engineering Best Practices: Do not treat data engineering as just writing scripts. Be ready to discuss version control (Git), unit testing for data pipelines, logging, and error-handling frameworks in Python.
• Master PySpark Optimization Techniques: Interviewers frequently ask about performance tuning. Be prepared to discuss partitioning, caching strategies, shuffling, and how to identify and resolve data skew issues in Spark.

• Be Ready for Ambiguity: In a consulting environment, client requirements can change rapidly. Demonstrate your ability to navigate ambiguity by asking clarifying questions before designing a database schema or pipeline during the interview.

Securing a Data Engineer position at Affine is an excellent opportunity to accelerate your career in cloud data engineering and consulting. The role offers exposure to diverse enterprise technologies, massive datasets, and the chance to solve complex data challenges for global brands. By mastering the fundamentals of Python, SQL, and cloud orchestration tools like Azure Data Factory and Databricks, you can set yourself apart as a highly competent and versatile engineer.

As you prepare, focus on building a deep, practical understanding of how data flows through modern enterprise systems. Practice writing clean code, explain your architectural decisions clearly, and highlight your ability to collaborate with both technical and business stakeholders. To gain deeper insights into compensation structures, company culture, and real-world interview experiences, you can explore additional resources on Dataford.

The compensation insights module above reflects the competitive market rates for data engineering talent. When navigating your offer discussions at Affine, remember to highlight your unique combination of software engineering discipline, cloud expertise, and consultative communication skills to secure a package that aligns with your experience and the value you bring to the firm. Good luck with your preparation!