Synchrony Data Engineer Interview Questions and Answers

One sql and one basic python dict question

Q1. Questions on big data mostly spark
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Q2. Project based questions
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Basic aptitude questions

Data structure and algorithms

Q1. Java, SQL questions
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Q1. Casual talk about roles
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Q1. PartitionBy vs bucketBy in spark
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Q1. Python data types, SQL medium to complex, Spark medium question
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Q1. SQL complex, Spark theory
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Q1. Basic HR questions
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Q1. Advanced sql on ctl
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Q2. Python data structure
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Q1. Data Engineering Concepts
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Q2. Build ETL pipeline on cloud
Ans. 
ETL pipeline on cloud involves extracting data from various sources, transforming it, and loading it into a cloud-based data warehouse.
• Use cloud-based ETL tools like AWS Glue, Google Cloud Dataflow, or Azure Data Factory to extract, transform, and load data.

• Design the pipeline to handle large volumes of data efficiently and securely.

• Utilize serverless computing and auto-scaling capabilities of cloud platforms to optimi...read more
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Q3. Writing sql queries to solve the logic
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Q4. Data Modelling techniques
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Data modelling techniques involve creating a visual representation of data relationships and structures.
• Identifying entities and their relationships

• Creating entity-relationship diagrams

• Normalizing data to reduce redundancy

• Using tools like ERwin, Visio, or Lucidchart

• Implementing data models in databases
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Q5. Performance Optimisation
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Q6. Data Warehouse implementation
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Data Warehouse implementation involves designing, building, and maintaining a centralized repository for storing and analyzing data.
• Designing the data warehouse schema to meet business requirements

• Extracting, transforming, and loading data from various sources into the warehouse

• Implementing data quality processes to ensure accuracy and consistency

• Creating data models and reports for analysis and decision-making

• Optimizi...read more
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Q1. Current job roles
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Q2. Data engineering concepts
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Q1. Explain about projects completed during academics and role performed?
Ans. 
During my academics, I completed a project on analyzing customer behavior using machine learning techniques.
• Developed a predictive model to forecast customer churn based on historical data

• Used Python for data preprocessing, feature engineering, and model building

• Collaborated with a team to gather requirements and present findings to stakeholders
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Q2. SQL based questions
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