Xendit Data Engineer Interview Preparation

Essential Technologies:

1. Programming Languages:

• Python (Excellent knowledge is required)
• SQL

1. Big Data Processing:

• Apache Spark (Excellent knowledge is required)
• Spark Streaming, Apache Flink, or Kafka for real-time data processing

1. Data Processing and Storage:

• Delta, Hudi, Iceberg (table formats)
• Apache Airflow for workflow orchestration
• Databricks environment, including Delta, Unity Catalog, DLT, SQL warehouse
• Trino (formerly PrestoSQL), Apache Druid, PostgreSQL, MongoDB, Elasticsearch, Snowflake

1. Data Quality and Testing:

• dbt (data build tool)
• Great Expectations for data quality checks

1. Data Governance and Security:

• Unity Catalog
• Apache Ranger
• Knowledge of data governance policies for security

1. Integration Tools:

• Kafka for building real-time applications
• Integration with modern data tools like Databricks, Airflow, OpenMetadata, Trino, Snowplow, Retool

1. CI/CD and Deployment:
2. CI/CD process (e.g., Buddy)

• Deployment to Kubernetes (K8s)

Preferred Technologies:

1. Fraud Detection:

• Experience in designing and integrating a scalable real-time fraud detection system

1. Infrastructure as Code (IaaC):

• Terraform, CloudFormation, or other IaaC tools

1. Cloud Platforms:

• AWS (or another cloud platform)

1. Data Security:

• Experience with sensitive data and ensuring secure access through a data governance tool

Responsibilities:

• Design and development of internal libraries (Python, Spark, dbt)
• Replication and transformation pipelines in close to real-time
• Data pipeline logic improvement (Python, Spark, Airflow)
• Data governance policy implementation (Unity Catalog, Apache Ranger)
• Enabling teams to build real-time applications on the Data lakehouse (Spark streaming, Kafka)
• Automation of common data requests (Retool, Flask)
• Data quality assurance through automated tests and data contracts (dbt, Great expectations)
• Deployment process improvement for various applications (Buddy)
• Collaboration with other data engineers, analysts, and business users
• Guiding junior engineers and setting engineering standards
• Incident detection and recovery time minimization, meeting metrics and SLOs
• Researching and integrating innovative technologies into the data infrastructure
• Advanced Python Concepts:
• Question: Can you explain the concept of metaclasses in Python and provide a scenario where you might use them?
• Answer: Metaclasses are classes for classes. They define how classes behave. They can be used for code generation, enforcing coding standards, and altering class behavior during creation.
  Question: Discuss the Global Interpreter Lock (GIL) in Python and its impact on multi-threading. How can you mitigate its effects in a multi-core system?
• Answer: The GIL allows only one thread to execute Python bytecode at a time, limiting the effectiveness of multi-threading for CPU-bound tasks. Mitigation can involve using multiprocessing instead of multithreading or using libraries written in C that release the GIL during execution.
  Question: Explain the use of decorators in Python. Can you provide an example of a practical use case in a real-world application?
• Answer: Decorators allow you to modify or extend the behavior of functions or methods. A practical use case is logging, where you can use a decorator to log function calls and their arguments.
  Data Handling:
• Question: What are Python generators, and how do they differ from regular functions? Provide an example of a scenario where using a generator would be advantageous.
• Answer: Generators are iterators that produce values on-the-fly. They use the yield statement to suspend and resume their state. They are memory-efficient for generating large sequences of data, as they produce values one at a time.
  Question: Discuss the differences between shallow copy and deep copy in Python. When would you use each?
• Answer: Shallow copy creates a new object but does not create new objects for nested structures. Deep copy creates a new object and recursively copies all objects found in the original. Use shallow copy when the structure is simple, and deep copy when dealing with complex, nested structures.
  Python Libraries:
• Question: Explain the key differences between NumPy and pandas. In what scenarios would you choose one over the other for data manipulation?
• Answer: NumPy is a numerical computing library, while pandas is a data manipulation and analysis library. Use NumPy for mathematical operations on arrays, and pandas for working with labeled data, tables, and time series.
  Question: How does the Global Interpreter Lock (GIL) impact the performance of parallel processing in Python? How can you achieve parallelism in Python, considering the GIL?
• Answer: The GIL limits the effectiveness of multithreading for CPU-bound tasks. To achieve parallelism, use multiprocessing instead of multithreading. The multiprocessing module allows parallel execution of Python processes, each with its own interpreter and memory space.
  Error Handling and Testing:
• Question: Describe the differences between try/except and try/finally in Python. When would you use one over the other?
• Answer: try/except is used to catch and handle exceptions, while try/finally is used to ensure that a block of code is always executed, regardless of whether an exception occurs. Use try/except for handling exceptions, and try/finally for cleanup operations.
  Question: How does unit testing work in Python, and what is the purpose of the unittest library?
• Answer: Unit testing in Python involves testing individual units or components of a program. The unittest library provides a framework for writing and running tests. It includes test discovery, fixtures, and assertions for verifying expected outcomes.
• spark :https://www.pass4future.com/questions/databricks/databricks-certified-associate-developer-for-apache-spark-3.0