Data engineer jobs are on a rise today due to the increase in demand for data science as a whole. Learn how to get one in this article.
In the present day, there are more job openings for a data engineer than a machine learning engineer due to the fact that most businesses are now planning to add a data-driven decision-making backbone to their organization rather than building a machine learning model. Not only that but legacy systems are now required to be migrated to newer technologies as well due to the rapid change in the technology landscape.
In this article, we will help you get ready for getting a data engineer job interview and for acing it as well. Here are the three things we suggest you do (excluding generic suggestions):
1. Understand what typical data engineer jobs look like
Although data engineering is supposed to be a linear field, there are many varieties of data engineer jobs out there. Some focus on building or maintaining big-data infrastructures for an organization and some may focus on migrating legacy systems or upgrading them.
So, before you even get started with data engineering, the first step is to understand the common requirements for a data engineer across multiple job openings. This helps you understand what data engineers share in common across many organizations and will help you reverse engineer your skills as needed. Plus, you get the added benefit of knowing what tools are currently popular in the data science industry for data engineer jobs.
Here is a general job requirement for a data engineer we’ve pieced together after looking at multiple openings:
- Engineer and build a data pipeline architecture for data storage and management.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies.
- Crawl websites and collect internet-based data.
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Keep our data separated and secure across national boundaries through multiple data centers and AWS regions.
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
- Work with data and analytics experts to strive for greater functionality in our data systems.
As you can see right away, Amazon Web Service (AWS) is the preferred cloud-based service for this job opening. So, a data engineer who has experience working with AWS or knows how to work with AWS is more likely to get hired than someone who doesn’t.
This is how you can quickly start understanding the market demand even before you start learning data engineering. Also, we suggest that instead of looking at the requirements of random data engineer jobs, try to look at the requirements from companies you want to work in and build your skill/toolset accordingly.
2. Focus on the common data engineering tools
Although there are many data engineering tools out there, you have to understand that most organizations only work with a handful of tools outside of the most common ones. So, learning the common ones first is a good approach to getting data engineer jobs.
Learning five different tools for working on the same problem is completely fine as long as you focus on first acing the common data engineering tools first. Here’s a comprehensive number of commonly used data engineering tools/programming languages:
- Python: Python is one of the most popular programming languages. It is known as the “lingua franca” of data science and is widely used for statistical analysis tasks. Fluency in python appears as a requirement in over two-thirds of data engineer job listings. Data engineers use python to code extract-transform-load (ETL) frameworks, API interactions, automation, and data munging tasks such as reshaping, aggregating, joining disparate sources, etc. It is easy to learn and has become the de-facto standard to data engineering because of its simple syntax and an abundance of third-party libraries.
- SQL: SQL is a standard language for storing, manipulating, and retrieving data in databases. It is one of the most important tools that help access, update, insert, manipulate, and modify data using queries and data transformation techniques. It is the key tool used by data engineers to create business logic models, execute complex queries, extract key performance metrics, and build reusable data structures.
- PostgreSQL: PostgreSQL is one of the most popular open-source relational databases. It is lightweight, highly flexible, highly capable, and is built using an object-relational model. It offers a wide range of built-in and user-defined functions, extensive data capacity, and trusted data integrity. It offers high fault tolerance and is designed to work with large datasets. So it has been an ideal choice for data engineering workflows.
- MongoDB: MongoDB is a popular NoSQL database. It is easy to use, highly flexible, and can store and query both structured and unstructured data at a high scale. It is much more flexible to handle unstructured data and stores them in easily understandable forms, unlike relational databases. Data engineers work with a lot of raw, unprocessed data. Features like a distributed key-value store, document-oriented NoSQL capabilities, and MapReduce calculation make MongoDB an excellent choice for processing huge data volumes preserving data functionality while allowing horizontal scale.
- Apache Spark: Apache Spark is an open-source analytics engine for large-scale data processing. It supports multiple programming languages and runs on multiple platforms, including Hadoop, Apache Mesos, Amazon EC2, Kubernetes, and hundreds of other data sources. It runs workloads faster, using a DAG scheduler, a query optimizer, and a physical execution engine. It can process terabytes of streams in micro-batches and uses in-memory caching and optimized query execution.
- Amazon Redshift: Amazon Redshift is a cloud data warehouse designed for large-scale data storage and analysis. Amazon Redshift allows Data engineers to combine exabytes of structured and unstructured data stored in the data warehouse, operational database, and data lake using standard SQL. It also allows data engineers to easily integrate new data sources within hours which reduces time to insight. Redshift saves the results of all queries run in the Amazon S3 data lake using open-source formats where additional analytics operations can be run using other analytic services like Amazon Athena.
- Apache Hive: Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data queries and analysis. It gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. The three important functionalities for which Hive is deployed are data summarization, data analysis, and data query. The query language supported by Hive is HiveQL. This language translates SQL-like queries to MapReduce jobs for deploying them on Hadoop.
- Snowflake: Snowflake is a popular cloud-based data warehousing platform that offers businesses separate storage and computes options, support for third-party tools, data cloning, and much more. It helps streamline data engineering activities by easily ingesting, transforming, and delivering data for deeper insights. Data engineers don’t have to worry about managing infrastructure, concurrency handling, etc with Snowflake. So they can focus on other valuable activities for delivering the data. In Snowflake, the data workloads scale independently from one another, making it an ideal platform for data warehousing, data lakes, data engineering, data science, and developing data applications.
- Amazon Athena: Amazon Athena is an interactive query tool that helps to analyze unstructured, semi-structured, and structured data stored in Amazon S3. It can be used for ad-hoc querying on structured and unstructured data using standard SQL. It is completely serverless which means there’s no need to manage or set up any infrastructure. Athena makes data engineers analyze large datasets in no time.
- Apache Airflow: Apache Airflow is an open-source workflow management platform, used as a solution to manage the company’s increasingly complex workflows. With the emergence of multiple cloud tools in modern data workflow, managing data between different teams and achieving data’s full potential seems challenging. So Apache Airflow has been a favorite tool for data engineers for orchestrating and scheduling their data pipelines. Apache Airflow helps to build modern data pipelines and offers a rich user interface to easily visualize pipelines running in production, monitor progress, and troubleshoot issues when required.
Also, always brush up your knowledge on the above-mentioned tools before going to an interview. This will help you become more confident about any questions related to the tools themselves.
3. Learn how to become a data system architect
Learning how to engineer data system architects may sound like a given when pursuing data engineering jobs but it isn’t. This actually differentiates high-paid data engineers from their counterparts.
Most people spend time learning different data engineering tools and forget the part about piecing all of the tools together. So, when the interviewer casually asks how to build a data system architecture for performing a certain task, the interviewee blanks out. In large companies, it is almost certain that you will also be asked a similar kind of question.
Just like a plumber knows which socket is needed for which connection and how taps should be installed along the way, you must also understand all the nuances about building robust data system architectures to get a chance at high-paying data engineering jobs.
So, if you’re trying to shoot for the stars with a dream data engineer job, focusing on data system architecture will help you get there. Furthermore, the time you spend learning about such architectures will help you for a lifetime since the knowledge is transferrable from one organization to another.
Hope this article was helpful for you. We excluded generic suggestions about tailoring your resume, building a portfolio, etc. assuming you already know about those.
If you have some suggestions of your own that you would want to include in this article, please feel free to comment down.
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