A data engineer is a responsible for designing, building, and maintaining the infrastructure that supports data processing and analysis for a company. At its core, a data engineer role is one that takes massive amounts of raw data and turns them into actionable insights for leaders, often helping to guide the direction of a company.
Data engineers have a few core competencies. Data processing—including the collecting, cleaning, and transforming data—is critical.
Because data engineers deal with such large volumes of data, their problem-solving skills are invaluable. Challenges involving data volume, complexity, and performance all require fluidity in this role. Applying technical expertise and analytical thinking, data engineers come up with effective solutions to deliver necessary data to leadership.
Finally, technical skills are essential for data engineers. A strong foundation in programming languages like Python, SQL, and Java is critical. Familiarity with data storage and data lake platforms may also be necessary. Knowledge of cloud platforms like AWS and Azure as well as data processing frameworks such as Hadoop and Spark are becoming critical, too.
But how easy is the discipline to learn? What questions should you expect in a data engineer job interview? We spoke with several experts to find out.
How can you learn to be a data engineer?
Our experts largely agree the best way to learn data engineering is learning to code, then familiarizing yourself with the platforms data engineers use. The discipline is code-centric, so it’s vital to know SQL, Python, and Java to start.
All agree that data engineers are, by nature, problem-solvers. “Tools like Hackerrank can be useful for polishing particular problem-solving skills,” notes Melissa Benua, VP of Engineering at mParticle, adding: “The best interviews are based on skills and experience that is generally picked up on the job, rather than on memorized algorithms or ritualized complex coding challenges. We often like to see candidates practice technical skills as well by taking advantage of free trials or credits in the cloud providers (especially GCP and AWS) to explore setting up their own basic ETL pipelines or simple services.”
What qualities does a great data engineer possess?
Jess Anderson, Managing Director of Big Data Institute, says the most-desired skills come down to the company’s specific needs. Anderson looks for “a solid understanding of the frameworks they're using. It's also key to be able to create complex systems for data.”
When hiring data engineers, Dan Prince, founder and CEO of Illumisoft, looks for the ability to communicate complicated ideas easily and efficiently. In other words, “soft skills” such as empathy and communication are key for data engineers. He also values their ability to grasp a problem, understand its context, and ask the right questions.
“I also expect that they have some self-initiated project experience,” Prince says. “Many kids will go through college or a degree curriculum without ever trying to put any of their knowledge to work outside of academia. I'm looking for people that are bold enough to try, and if they have sold their services well still a student, that's even better.”
Rudolf Höhn, Data Scientist at Unit8 SA, tells Dice: “We want our data engineers to be technical, but also be understandable by less technical people, such as a project client. Our engineers may face clients; hence, it is important that they can articulate ideas in a clear way, especially in front of a business audience.”
What mandatory skills should a data engineer possess?
“All data engineers should be able to code,” says Benua Melissa Benua, “though the language itself doesn’t matter. Candidates should also be familiar with distributed systems design principles. Likewise, they should have solid database experience. They should be skilled at writing SQL—especially high-performance and cost-efficient queries for processing large datasets—and basic database administration. Knowledge of AI/ML is a bonus but is generally not considered a requirement.”
Tee Selesi, talent acquisition manager for edge services provider StackPath, adds: “Our need to analyze data sets and find trends is ever-increasing, so we are interested in interviewing candidates with software programming, data modelling, and data partitioning backgrounds. A successful candidate would also need to have a firm understanding of the optimization/constraints that come with large datasets.”
Anderson adds: “They should have a least intermediate-level programming skills and experience with at least one batch processing system such as Apache Spark.”
Prince adds: “You have to have experience using the tools of the trade like Apache Hadoop and Spark, C++, Amazon Web Services, and Redshift. You also must know a few different database systems, both relational and non-relational. You must understand data warehousing solutions, ETL tools, machine learning, and data APIs. You need to know and understand some Python, some Java, and some scale-up programming languages. Other than communication skills, presentation skills and self-initiated experience, a plus would be a good understanding of distributed systems and knowledge of algorithms and data structures.”
What are some sample data engineer interview questions?
Here’s the aggravating part of job interview questions: companies will want radically different things out of their data engineers, and so they’ll ask totally different questions than another company advertising for a similar position. If you know the fundamentals of the profession and you’ve kept your skills up to date, you should be able to field most technical questions.
Many sites online offer up sample data engineer interview questions, including (but certainly not limited to) Simplilearn, DataCamp, Indeed, arc.dev and more. If you’re looking for a comprehensive list of questions to compare your knowledge against, Anderson, Benua, Selesi, and CEO of Educative Fahim ul Haq offer these sample questions for the data engineer interview:
- Have you ever transformed unstructured data into structured data?
- How would you validate a data migration from one database to another?
- What is Hadoop? How is it related to Big Data? Can you describe its different components?
- Which Python libraries would you utilize for proficient data processing?
- Do you consider yourself database- or pipeline-centric?
- Tell us about a distributed system you've built. How did you engineer it?
- How do you handle conflict with coworkers? Can you give us an example?
- What do *args and **kwargs Mean?
- Design a video streaming service like YouTube or Netflix.
- Design a consumer-facing data storage solution like Google Drive or Dropbox.
- Do you have experience using PostgreSQL or other RDBMS and general understanding of NoSQL databases?
- Do you have experience scripting ETL workflows on Linux/Unix Python and/or Golang?
- Do you have experience in building and maintaining data pipelines using Kakfa (or similar)?
- Do you have experience in writing analytic (OLAP) queries in SQL?
- Do you have Hands-on experience with Spark?
- Do you have experience with cloud platforms, such as GCP, AWS, and Azure?
- Do you have experience with Docker and/or Kubernetes?
- Walk me through one of your data engineering projects, preferably one where you took the assignment from idea stage through implementation and owned it into production.
- Describe to me how a pipeline that reads data from a queue and periodically uploads data to S3 might work. How would you scale it?
- Design a SaaS platform that might compete with Google Analytics. How would it scale? What tradeoffs might you make in which parts of the problem you solve first?
- Given a dataset, write SQL to answer these relevant business questions.
- Why would you choose S3 versus a NoSQL database?
- Why would you choose a NoSQL database versus a relational database?
- How would you go about diagnosing a performance issue in a Spark job?
- What is a shuffle sort?
- How does Spark differ from S3?
- As a take-home coding assignment (2-3 hours): Write a pipeline that reads input files and produces aggregated stats (like what group-by queries do in a database but write the process themselves). Depending on the level, the deliverable is either a basic pipeline or some extendable/scalable solutions.
In an interview process, hiring managers are often looking for things not listed in the job description. Early interviews are assessments of your soft skills as much as they are an overview of your technical skills.
It’s important to be an effective communicator who can discuss deeply technical points of interest with non-technical audiences. It’s common for tech savvy professionals to fall into the trap of getting “in the weeds” during an interview process, discussing things on a technical level that the interviewer may not fully grasp.
Even if you’re sure the audience is one that might understand tech-speak, tread lightly into that territory. Even the most technical folk want to see a data engineer that can communicate effectively to a broad set of people. With that in mind, here are some additional sample questions and answers for you to explore:
Sample question: "Describe a data pipeline you've worked on, including the components involved, the type of processing used (real-time or batch), and any optimization techniques you implemented."
Sample answer: "In my previous role at [Company], I was responsible for designing and implementing a data pipeline to process customer interaction data from our call center. The pipeline consisted of the following components:
- Data Source: The primary source of data was our call center's database, which contained records of incoming and outgoing calls, call duration, and customer information.
- Data Ingestion: We used Apache Kafka as a distributed streaming platform to ingest data from the database in real-time. Kafka's ability to handle high-throughput and low-latency made it ideal for our use case.
- Data Transformation: Once the data was ingested into Kafka, we employed Apache Spark to perform various transformations, such as cleaning, filtering, and aggregating data. Spark's distributed processing capabilities allowed us to efficiently handle large datasets.
- Data Storage: The transformed data was then stored in a data warehouse, such as Amazon Redshift, for long-term storage and analysis.
- Data Delivery: Finally, we used tools like Apache Airflow to orchestrate the entire pipeline and ensure timely delivery of data to downstream consumers, such as data analysts and scientists.
“Since our use case required near real-time analysis of customer interactions, we opted for a real-time processing approach. To optimize the pipeline's performance, we implemented several techniques:
- Partitioning: We partitioned data in Kafka and Spark to improve parallelism and reduce processing time.
- Caching: We cached frequently accessed data in memory to minimize I/O operations.
- Indexing: We created appropriate indexes in the data warehouse to optimize query performance.
- Compression: We compressed data to reduce storage costs and improve network transmission efficiency.
“By carefully considering these components and optimization techniques, we were able to build a robust and scalable data pipeline that met the needs of our business.”
Lucas Botzen, founder at RiverMate, tells Dice: “Performance optimization of data pipelines can be achieved through several techniques. One approach is to implement parallel processing, which allows multiple tasks to be executed simultaneously, reducing overall processing time. Another technique involves optimizing the data formats used, such as using columnar storage formats like Parquet or ORC, which are more efficient for read-heavy operations. Caching intermediate results can also improve performance by avoiding repeated computations.
Additionally, using batch processing for large volumes of data and real-time processing for smaller, time-sensitive tasks can balance the workload and improve efficiency. Monitoring and optimizing resource allocation based on workload requirements ensure that pipelines run smoothly and without bottlenecks.”
Sample question: "Describe a complex SQL query you've written and the steps you took to optimize its performance."
Sample answer: "In my previous role at [Company], I was tasked with analyzing customer behavior data to identify patterns and trends. I wrote a complex SQL query that involved joining multiple tables, performing aggregations, and applying window functions.
“The query first joined customer information, purchase history, and website interaction data. Then, I used window functions to calculate metrics like customer lifetime value, recency, and frequency. Finally, I applied filtering and sorting to isolate the most valuable customers.
“To optimize the query's performance, I took several steps:
- Indexing: I created indexes on frequently used columns, such as customer ID and purchase date, to improve query execution speed.
- Query Optimization: I used EXPLAIN to analyze the query execution plan and identify potential bottlenecks. This helped me rewrite the query to avoid unnecessary full table scans and improve selectivity.
- Data Partitioning: I partitioned the tables based on customer ID to distribute data across multiple nodes and improve query parallelism.
- Materialized Views: For frequently used aggregations, I created materialized views to pre-calculate the results and avoid expensive computations at query time.
“By carefully considering these optimization techniques, I was able to significantly improve the query's performance and deliver valuable insights to my team."
Sample question: "How do you approach data modeling for a large-scale data warehouse or data lake, ensuring both scalability and efficiency?"
Sample answer: "Data modeling is a crucial step in building a scalable and efficient data warehouse or data lake. It provides a blueprint for organizing and storing data, ensuring that it can be accessed and analyzed effectively.
“When designing a data model for large-scale data, I focus on the following principles:
- Normalization: Normalization helps to reduce data redundancy and ensure data integrity. By breaking down data into smaller, normalized tables, we can avoid inconsistencies and anomalies. However, it's important to balance normalization with performance considerations, as excessive normalization can lead to more complex queries.
- Denormalization: In certain cases, denormalization can improve query performance by reducing the number of joins required. This is especially useful for frequently accessed data that is often used together.
- Partitioning: Partitioning data into smaller, more manageable chunks can improve query performance and scalability. By partitioning data based on time, geography, or other relevant criteria, we can optimize data access and storage.
- Clustering: Clustering related data together can also enhance query performance. By clustering data based on frequently used patterns or relationships, we can minimize the amount of data that needs to be scanned during queries.
- Data Types: Choosing the appropriate data types for each column is essential for efficient storage and retrieval. Using the most compact data types possible can reduce storage costs and improve query performance.
“Additionally, I consider the specific requirements of the data warehouse or data lake when designing the data model. Factors such as the volume of data, query patterns, and performance requirements will influence the choice of modeling techniques.
“By carefully considering these principles and the specific needs of the data environment, I can create a data model that is both scalable and efficient, supporting the organization's data analysis and reporting needs.”
Rafay Baloch, CEO and Founder of REDSECLABS, adds: “working on data modeling tasks in databases like star schemas or snowflake models and galaxy structures have functions; however security is crucial to safeguard valuable data assets effectively. Maintaining high data quality within a data warehouse requires implementing top notch approaches such, as verifying the data accuracy through validation procedures and cleansing processes while also monitoring for any risks or unauthorized activities carefully.”
“Handling schema evolution in data pipelines involves using schema registries, flexible formats like Avro or Parquet, and version control within ETL processes,” notes Vladislav Bilay, DevOps engineer at Aquiva Labs, “This strategy ensures that pipelines can process both old and new data formats without disruption, maintaining data integrity throughout changes.
“Performance optimization in data pipelines includes minimizing data movement, reducing I/O operations, and optimizing query execution. Techniques like partitioning, indexing, and in-memory processing, along with choosing the right data formats, are crucial for maintaining efficiency.
“ETL challenges, such as handling schema changes, managing large data volumes, and ensuring data quality, require robust and flexible pipeline designs. Strategies include versioned schemas, incremental processing, and comprehensive error-handling mechanisms.
“Batch processing large datasets requires using distributed frameworks like Apache Spark, optimizing resource use, and implementing fault tolerance. Monitoring and tuning the pipeline are crucial to maintaining performance and scalability.
Sample question: “Describe an ETL process you've implemented, including the tools used and the challenges you faced. How did you evaluate and select the appropriate ETL tools for the project?"
Sample answer: "In my previous role at [Company], I was responsible for implementing an ETL pipeline to extract data from multiple source systems, transform it, and load it into a data warehouse for reporting and analysis.
“The ETL process involved the following steps:
- Extraction: We used a combination of database connectors, APIs, and file-based extraction methods to extract data from various sources, including relational databases, flat files, and cloud-based services.
- Transformation: We employed ETL tools like Informatica PowerCenter and Talend to perform data cleansing, validation, and transformation tasks. These tools provided a graphical interface for building and managing ETL workflows.
- Loading: The transformed data was then loaded into a data warehouse, such as Amazon Redshift, using bulk loading techniques for optimal performance.
One of the main challenges we faced was dealing with data quality issues, such as missing values, inconsistencies, and duplicates. To address these issues, we implemented data cleansing and validation rules within the ETL process.
“When selecting ETL tools, we considered several factors:
- Functionality: The tools needed to support the specific ETL requirements of the project, including data extraction, transformation, and loading capabilities.
- Scalability: The tools should be able to handle large volumes of data and scale as the data warehouse grows.
- Integration: The tools should integrate seamlessly with our existing data infrastructure, including databases, cloud platforms, and other applications.
- Ease of Use: The tools should be user-friendly and provide a graphical interface for building and managing ETL workflows.
- Cost: The cost of the tools should be considered within the project's budget constraints.
“By carefully evaluating these factors, we were able to select the most appropriate ETL tools for our project and successfully implement the ETL pipeline."
Baloch notes: “When dealing with the ETL process challenges usually revolve around managing data volumes in a secure manner. To tackle these issues, one needs to plan meticulously and enforce strict access controls while also conducting regular audits. For handling datasets through batch processing, it is recommended to implement strong error handling and logging systems to swiftly identify and address any security breaches.”
“Common challenges during the ETL process include data inconsistency, handling large volumes of data, and ensuring data security and compliance,” Botzen adds. “Data inconsistency often arises from diverse data sources with varying formats and quality standards. To address this, it is crucial to implement robust data validation and cleansing procedures. Managing large data volumes requires scalable infrastructure and optimized ETL processes that can handle data growth without degradation in performance. Ensuring data security and compliance involves implementing encryption, access controls, and auditing mechanisms to protect sensitive information.”
Sample question: "Describe a project where you had to implement machine learning techniques on a large-scale dataset. How did you handle the challenges of unstructured data and ensure data governance throughout the project?"
"In my previous role at [Company], I was involved in a project to develop a recommendation engine for our e-commerce platform. The goal was to personalize the customer experience by suggesting products that were likely to be of interest to them.
“To build the recommendation engine, we utilized a combination of collaborative filtering and content-based filtering techniques. Collaborative filtering involved analyzing customer purchase history and behavior to identify similar users and recommend products that they had purchased. Content-based filtering involved analyzing product attributes and customer preferences to recommend products with similar characteristics.
“One of the main challenges we faced was dealing with unstructured data, such as product descriptions and customer reviews. To address this, we implemented natural language processing techniques to extract relevant features from the unstructured text data. We used tools like NLTK and spaCy to perform tasks such as tokenization, stemming, and sentiment analysis.
“Ensuring data governance was also a critical aspect of the project. We implemented data quality checks and validation rules to ensure the accuracy and consistency of the data. We also established data lineage to track the origin and transformation of data throughout the pipeline. Additionally, we implemented data security measures to protect sensitive customer information.
“By carefully considering these factors, we were able to build a robust and scalable recommendation engine that provided personalized recommendations to our customers and improved user engagement."
Botzen tells Dice: “Ensuring data quality in a data warehouse starts with setting clear data governance policies. It is crucial to implement validation rules and automated checks that can identify inconsistencies or errors in data as it enters the warehouse. Regularly scheduled data quality audits and monitoring are necessary to detect anomalies and ensure data integrity. Additionally, adopting a robust ETL (Extract, Transform, Load) process with well-defined data cleaning and transformation steps helps maintain high data quality. Finally, establishing a feedback loop with data users allows for continuous improvement and quick resolution of any issues that may arise.”
In an interview process, hiring managers are often looking for things not listed in the job description. Early interviews are assessments of your soft skills as much as they are an overview of your technical skills.
It’s important to be an effective communicator who can discuss deeply technical points of interest with non-technical audiences. It’s common for tech savvy professionals to fall into the trap of getting “in the weeds” during an interview process, discussing things on a technical level that the interviewer may not fully grasp.
Even if you’re sure the audience is one that might understand tech-speak, tread lightly into that territory. Even the most technical folk want to see a data engineer that can communicate effectively to a broad set of people.
Conclusion
The data engineer role evolves as technology advances. Continuous learning and adaptation are mandatory soft skills, and a strong foundation in technical skills is also crucial.
Data engineers should always be looking to implement platforms that both satisfy business needs and make their day-to-day lives easier. Platforms and technologies will always require human interaction, which is why problem solving and technical prowess are such critical skills for the data engineer.
