Amazon Data Engineer 1 Interview Questions You Should Know in 2026

The Amazon data engineer 1 interview questions guide is an informative and concise resource that helps you crack interviews. Amazon data engineer 1 is an entry-level junior role.

Amazon data engineer 1 is the foundational stage of a data engineer’s career at Amazon. You will be assigned to a project and focus on core tasks, learning specific tech stacks. You will operate in an established framework with growth potential.

As you progress and show exemplary work, you grow into more complex responsibilities that form the core of Amazon’s data engineer work. However, it is essential to first crack the Amazon data engineer 1 interview questions, and this guide helps you to do that.

This blog explains the roles and responsibilities of an Amazon data engineer 1, and presents several key topics and sample questions. Typical compensation for an Amazon data engineer 1 is $220k+. This is your start on the long path of growing as a data engineer at Amazon.

Key Takeaways

What Amazon Looks for in a Data Engineer 1

Amazon looks at Data Engineer 1 candidates with strong SQL, Python/Java/Scala coding, data modelling star/ snowflake schemas, ETL/data pipeline skills. You should have good knowledge of big data, such as Spark, Hadoop, AWS services, S3, and Redshift.

Let us look at some of the essential skills for an Amazon data engineer 1.

Education: A BS or MS degree from a reputed college in computer science, Engineering, IT, or a related technical field, with high grades. Project work in Amazon or data engineering is impressive.

Experience: 0-1-year experience with medium-sized firms specializing in data engineering work. Many students are selected through campus placements, and based on performance, interns may be offered a job.

Technical skills: Some level of experience and exposure with programming languages such as Python, Java, Object-Oriented Design, data structures, Algorithms, data engineering process and tools, ETL/ELT processes, data warehousing, AWS.

Behavior: Amazon wants data engineers with good behavioral patterns, teamwork, pleasant manners, and people who are ready to resolve conflicts amicably.

Core Responsibilities of Amazon Data Engineer 1

As explained in the Amazon data engineer 1 interview questions, the engineer works under instruction to build and maintain scalable data pipelines (ETL/ELT) using AWS services S3, Redshift, Glue, and Kinesis.

The role sources, transforms, and loads massive datasets for analytics, for precise data quality, performance, and reliability. Let us look at some of the Amazon data engineer 1 interview questions.

• Data Pipeline Development: Amazon data engineer 1 is expected to design, build, and optimize scalable, reliable data pipelines (batch & streaming) using Python/Java, SQL, and AWS services (Glue, Kinesis, EMR).
• ETL/ELT Processes: Data Engineer 1 at Amazon creates and manages Extract, Transform, Load (ETL) jobs to clean, aggregate, and prepare messy data for analysis.
• Data Modeling & Storage: They are expected to develop data models and manage storage solutions (S3, Redshift, DynamoDB) for efficient querying and cost-effectiveness.
• Performance and Optimization: As mentioned in the Amazon data engineer 1 interview questions guide, the role is expected to monitor, troubleshoot, and tune pipelines and databases for speed, cost, and reliability.
• Collaboration: Amazon data engineer 1 supports data scientists, business intelligence engineers, and Analysts by providing production-ready datasets and tools.
• Code Quality: They need to write clean, maintainable code, participate in code reviews, and develop unit tests

Amazon Data Engineer 1 Interview Questions

Amazon data engineer 1 interview questions focus on the candidate’s knowledge of theory and hands-on experience with technology. Questions will cover programming languages with coding, data engineering process and tools, and Amazon-developed tools.

Remember to:

• Ask Clarifying Questions about Volume, latency, and business goals.
• Declare assumptions you make
• Mention your choices and trade-offs, such as SQL vs. Spark, Cloud vs. On-prem.
• Use visuals, draw sketches to explain your architecture.
• Link technical choices to business value.

Coding questions may be administered in an AI environment. In later rounds, interviewers. Let us look at the Amazon data engineer 1 interview questions and the topics.

Coding Amazon Data Engineer Interview Questions

The coding Amazon data engineer 1 interview questions focus mainly on Python, Java, C++, JavaScript, Go, Rust, Ruby, and others. These languages help to run Amazon processes and build components.

Let us look at coding Amazon data engineer 1 interview questions.

Python: Questions will be on language features, data structures, and data manipulation libraries like pandas and NumPy.

• When and why will you use Pandas and NumPy?
• Explain the process of handling missing values in a pandas DataFrame. (df.isnull().sum() and df.fillna() or df.dropna()).
• Explain the process of building a modular ETL pipeline in Python and handling failures using try-except blocks and robust logging.
• How will you connect to a SQL database such as PostgreSQL using psycopg2 or SQLAlchemy?
• How will you prevent SQL injection using parameterized queries?

Java: Java questions are on object-oriented programming (OOP) principles, multithreading, and the use in big data frameworks like Hadoop and Spark.

• What are encapsulation, inheritance, polymorphism, and abstraction?
• How do you differentiate between similar-sounding keywords?
• What is the difference between Heap and Stack memory, and how does Java handle garbage collection?
• Explain the differences between HashMap and HashTable, and explain how HashMap handles collisions (chaining).
• How will you implement algorithms like binary search to find a missing number in an array, or reverse a string?

SQL and Data Modeling Amazon Data Engineer Interview Questions

Amazon data engineer 1 interview questions on SQL interviews focus on performance, data modeling, optimization, and real-world problem-solving. Questions will be asked on window functions, indexing, CTEs, ETL processes, and database design principles.

Here are common SQL interview questions for data engineers.

Foundation Concepts

• What are the differences between WHERE and HAVING clauses?
• What are the different types of JOINs? Differentiate between INNER, LEFT, RIGHT, FULL OUTER, CROSS, and SELF JOINs, and when to use each.
• Explain the difference between UNION and UNION ALL?
• What are DELETE, TRUNCATE, and DROP
• What are primary and foreign keys?
• What are Window Functions?
• When is a Common Table Expression used?
• How will you find and remove duplicate rows in a table?
• Calculate a moving average or running total.
• Explain how Indexes work and when they might hurt performance?

Performance

• Explain the working of Indexes and when they can hurt performance?
• What methods are used to optimize a slow-running query?
• What are the differences between OLTP and OLAP systems?
• Explain ACID properties in database transactions.
• Describe normalization and denormalization.

ETL and Data Pipeline Design Amazon Data Engineer Interview Questions

ETL and data pipeline design, Amazon data engineer 1 interview questions will be on ETL, batch, streaming, and schema evolution. The question will also be on design scenarios for e-commerce, clickstream, CDC pipelines, real-time analytics, data quality checks, deduplication, and error handling.

Core Concepts

• ETL and ELT: Explain differences, use cases, and modern trends of ELT in data lakes/warehouses.
• Batch vs. Streaming: When do you use and compare for latency, volume, and complexity?
• Data Modeling: Explain Star/Snowflake schemas, denormalization, and data warehousing concepts such as Medallion Architecture).
• Data Quality: What are the common checks for nulls, uniqueness, range, validation, and data lineage?
• Scalability and Performance: Describe Partitioning, indexing, sharding, and distributed processing.

Pipeline Design Scenarios

• E-commerce Pipeline: Design schemas for orders, user activity, incremental loads with UPSERT logic.
• Real-time Analytics: Design system for stock prices, user clickstreams with Kafka, Spark Streaming, and Watermarking.
• Change Data Capture: What is Log-based capture with Debezium, handling deletes, schema changes?
• IoT/Sensor Data: How is Ingestion, large volumes, and potential for data skew done?
• ML Platform: Describe data prep pipeline for models with feature stores, data versioning.
• Data Lakehouse: Design a layered architecture for Bronze, Silver, and Gold.

Scenario-Based Questions

• Schema Evolution: Explain strategies for handling source schema changes, such as adding columns and changing types.
• Data Consistency: How do you ensure data integrity during retries, handling duplicates (idempotency?
• Error Handling and Recovery: What will you do when jobs fail? Explain retries, backoff, and partial failures.
• Backfilling and Late Data: Describe processing historical data, handling out-of-order events.
• Orchestration: What is the role of Airflow, dependency management, and scheduling?
• Monitoring and Alerting: Explain the process of setting up alerts and debugging production issues.

ETL/ELT and Tools

• Explain the steps involved in Extraction, Transformation, Loading.
• What is your experience with Informatica, Talend, Apache NiFi, AWS Glue, Azure Data Factory, dbt?
• Explain data integration strategies for Batch and Real-time.
• How do you use Python/SQL to handle large files with chunking, window functions, and exception handling?

Data Modeling Amazon Data Engineer 1 Interview Questions

Data Modeling Amazon data engineer 1 interview questions cover key foundational concepts, normalization, keys, relationships, schema types such as star, snowflake, and handling specific challenges.

Let us look at the data modeling Amazon data engineer 1 interview questions.

• Explain the three main types of data models.
• Compare Normalization vs. Denormalization and define 1NF, 2NF, and 3NF.
• What are the differences between Primary Keys, Foreign Keys, Composite Keys, Natural Keys, and a Surrogate Key?
• What are the differences between the Star and Snowflake Schemas?
• Compare Fact and. Dimension Tables
• How does modeling for NoSQL databases differ from relational databases?
• Explain the process of handling schema changes in data pipelines without breaking downstream systems, using formats like Parquet or Avro.
• Compare Online Transaction Processing (optimized for frequent write/update operations) with Online Analytical Processing.
• Describe the process to model a schema for user watch history supporting multi-device streaming and recommendation training.
• Design a schema to track ride completions, pricing, and driver earnings. How would you ensure the fact table scales with millions of daily trips?
• What entities and attributes are used when creating a data model for a bank’s customer risk profile?

Data Warehousing and Storage Amazon Data Engineer 1 Interview Questions

Data warehousing and storage, Amazon data engineer 1 interview questions cover fundamental concepts, schemas, Star, and Snowflake. Questions will also be on key components such as Fact/Dimension tables, Data Marts, ETL/ELT processes and tools, Informatica, Talend, dbt, AWS Glue.

Core Concepts

• What are the differences, and why are data warehouses OLAP-focused?
• What is Dimensional Modeling? How does it differ from relational (ER) modeling?
• Explain Star Schema vs. Snowflake Schema: Pros, cons, and when to use each.
• What are Fact Tables? Explain the Types of transactional, snapshot, accumulating, and characteristics.
• What are Dimension Tables? Explain slowly changing dimensions – SCDs, junk, conformed, role-playing.
• What are Data Marts and their purpose?
• Explain the Operational Data Store and how it fits?
• Design a data warehouse for an e-commerce company.
• How will you manage rapidly changing product attributes?
• Explain the process of troubleshooting slow-running analytical queries.

Architecture and Design

• Describe Data Warehouse Architecture and Common layers for staging, integration, and access.
• Compare Data Lakes and Data Warehouses. What are the key differences in structure, schema, use cases, and data types?
• What is Metadata Management, its importance, and strategies?
• Explain Data Versioning techniques like SCDs, Delta Lake.
• How do you carry out performance tuning? Explain indexing, partitioning, and materialization?
• Describe data partitioning and clustering, and the strategies for large datasets.
• How do you ensure data accuracy and compliance?

Data Engineering Tools Amazon Data Engineer 1 Interview Questions

Data engineering tools, Amazon data engineer 1 interview questions cover data processing methods, programming languages such as Python, R, libraries (Pandas, NumPy, Scikit-learn, TensorFlow), databases, BI platforms Tableau, and Power BI.

Questions will also be on big data frameworks, Hadoop, and Spark. Machine learning with DataRobot, H2O.ai, aids tasks from data cleaning and analysis to visualization and model building for insights and predictions.

Core Concepts

What are data engineering and data analytics?

Explain Machine Learning principles with Supervised vs. Unsupervised Learning, Gradient Descent, Bias-Variance Trade-off, Overfitting/Underfitting, and prevention.

What are Type I/II errors, p-values, correlation, covariance, and sampling?

Algorithms and Models

• Explain with examples the linear regression assumptions and logistic regression.
• What are decision trees, random forests, and how do they work?
• What is clustering, K-Means, and cluster analysis?
• Explain Support Vector Machines, the hyperplane concept, and the kernel trick.
• What are ensemble methods, such as bagging and boosting?
• What are dimensionality reduction and principal component Analysis?

Tools and Libraries

• How do you use Matplotlib and Seaborn to create static and attractive plots?
• Explain the process of using TensorFlow and PyTorch for deep learning.
• Describe Scikit-learn, used for classic machine learning algorithms.
• How do you use R for statistical analysis and visualization?
• Describe the processes of Tableau, Power BI, and Kibana for data visualization and reporting.
• How will you use DataRobot and H2O.ai for automated machine learning?

Big Data Tools Amazon Data Engineer 1 Interview Questions

Big data tools Amazon data engineer 1 interview questions focus on big data concepts, tools, and handling big data. Let us look at some interview questions on big data.

Core Concepts

• What are the 5 V’s of Big Data?
• Describe HDFS storage, YARN resource management) and MapReduce processing.
• Explain the process of file splitting into blocks of default 128 MB, and replicated default 3x for fault tolerance.

Data Processing

• Compare Spark with MapReduce.
• What is data skew? Explain the process of handling uneven data distribution with salting keys or custom partitioning to balance node workloads.
• What are the differences between storing raw, unstructured data in a data lake and processed, structured data in a data Warehouse?
• Describe how systems like Delta Lake or Parquet handle changes in data structure over time without manual reloading.
• Compare the processing of large historical blocks, Batch/ Hadoop, with real-time, low-latency processing with Streaming, Kafka, or Flink.
• Discuss Lambda and Kappa Architecture patterns to combine or simplify batch and stream processing.
• What is speculative execution?

AWS Data Engineering Tools

• Explain the use of Amazon S3, Amazon Redshift, Amazon RDS, and Amazon DynamoDB for data storage.
• How do you use AWS Glue, Amazon Kinesis, Amazon EMR, and AWS Lambda for data ingestion and processing?
• How do you use Amazon Athena, Amazon QuickSight, and Amazon CloudWatch for analytics and monitoring?

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Conclusion

The blog presented a comprehensive set of Amazon data engineer 1 interview questions. Questions covered several key topics on data engineering skills that Meta expects.

While you have the experience and qualifications, confidence and presentation skills are also important. Interviews are tough, and you need expert guidance to help you crack the questions. All the stages of the data engineer 1 interview process are important.

However, this is the starting point in the interview process. At Interview Kickstart, we have several domain-specific experts who have worked for Meta and top-tier tech firms.

Let our experts help you with the Amazon data engineer 1 interview questions. You have much better chances of securing the coveted job.

FAQs: Amazon Data Engineer 1 Interview Questions

Q1. What do you avoid in the Amazon data engineer 1 interview?

In the interview, avoid negative talk about employers and colleagues. Speak about positive answers that display your ability to look beyond, analyze, and improve from feedback.

Q2. How do you crack an Amazon data engineer 1 Interview?

To crack behavioral interviews, prepare use cases with the STAR framework. The stories should be about data engineers’ work in your projects, college, or internship. Practice the stories by recording yourself. Structure the responses, and be concise with your contribution.

Q3. Are Amazon data engineer 1 interviews tough?

Yes. While the data engineer 1 questions will not be on advanced practices, you should prepare by reading about theory and implementations.

Q4. What is the method for the interviews?

In behavioral interview questions, follow the STAR approach. Speak of the efforts put in by your team members.

Q5. What is the acceptance rate of Amazon Data Engineer 1 candidates?

The acceptance rate is less than 2%. However, this should not frustrate and dishearten you. Aim to be among the 2% who are selected.

References

1. Using the STAR method for your next behavioral interview
2. Use the STAR Interview Method to Land Your Next Job