Data Engineers play a crucial role in ensuring that data scientists and other stakeholders have access to accurate, reliable, and readily available data at the required cadence for their analyses or solution implementation. This necessitates advanced skills in ETL (extract, transform, and load) systems, as well as a basic understanding of machine learning and algorithms.
Data engineering domain is closely related to data science and software engineering. As a data engineer, you will collaborate closely with data architects, data scientists, machine learning engineers, and CTOs.
To get hired, it is essential to demonstrate that you possess the right set of skills. In this article, we will examine the technical skills needed to transition into data engineering.
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Essential Technical Skills for Data Engineering Jobs
As an aspiring data engineer, you’ll want to show a few key skills to employers. Here we list the critical skills that one should possess to build a successful career in Data Engineering.
Programming
High-level programming languages are essential skills for understanding data engineering and pipelines. Java and Scala are used to write MapReduce jobs on Hadoop, while Python is suitable for data analysis and pipelines. Ruby is also a popular application glue across the board.
Data engineering relies heavily on programming languages, particularly for statistical modeling and analysis, data warehousing, and pipeline construction. Approximately 70% of data engineering roles require Python as a necessary skill, followed by SQL, Java, Scala, and other programming skills such as R, Perl, Shell Scripting, etc.
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Database Management
Companies rely on database-centric data engineers to perform tasks distributed across multiple databases. Data engineers primarily focus on analytics databases and collaborate closely with data solutions architects and data scientists. They work on data warehouses and develop table schemas.
Data engineers should possess extensive knowledge of database management and be proficient in both relational and non-relational databases.
Relational databases are SQL databases, while NoSQL databases can store non-relational data.
- SQL – Structured Query Language (SQL) is utilized for executing ETL system tasks. It is the database programming language for querying relational databases and is an essential component of data engineering.
- NoSQL – This type of database can store non-relational data, such as document stores (in JSON format) like MongoDB, and graph databases that use nodes to store data entities, such as Neo4j.
Most data engineering roles require SQL and NoSQL skills, followed by expertise in AWS Redshift, MongoDB, AWS S3, Cassandra, GCP BigQuery, and more.
ETL (Extract, Transform, and Load) Systems
ETL—Extract, Transform, Load is the process through which raw data is extracted from databases and other sources, then transferred and loaded into a single repository, such as a data warehouse or business intelligence platform.
Data engineering significantly simplifies the work of data science teams through ETL. It is the process that helps extract the right data for analysis and problem-solving, making it an essential skill for data engineers.
Some popular ETL tools used by data engineers include Xplenty, Informatica, Stitch, PowerCenter, AWS Glue, Alooma, and Talend.
Data Warehousing Solutions
Data warehouses and ETL help organizations leverage big data in a purposeful manner. Big data projects can be enormously complex and very time-consuming, requiring extensive contributions from data engineers to work on the vast amounts of data and skills in finding data warehousing solutions.
Data warehouses store enormous volumes of current and historical data and since this data is ported from extensive sources like CRM system, accounting software, and ERP software, most companies require data engineers to be familiar with the data storage tools. So that stakeholders can use the data for reporting, analytics, interactive visualizations and data mining.
Most employers expect entry-level engineers to be familiar with Amazon Web Services (AWS), a cloud services platform with an entire ecosystem of data storage tools like MarkLogic, Oracle, Amazon RedShift, etc.
Big Data Tools
Data engineers don’t just work with the regular data. Big data engineers work with a massive amount of both structured and unstructured data. Since big data is a crucial tool for data science and Machine learning teams, it becomes important for data engineers to know how to store, process, clean, and extract information from the data.
Big Data technologies, such as Apache Spark, Apache Kafka, Hadoop, Hive, ELK Stack, great_expectations, Segment, Snowflake, Cassandra, are in high demand and data engineers know how to work with these tools to handle huge data sets and identify patterns and trends within them.
Real-time processing frameworks: Some frameworks to know are Apache Spark, Hadoop, Apache Storm, Flink, and more.
- Data ingestion: It is one of the crucial parts of data engineering skills. Data ingestion is the process of moving data from one or more sources to a destination source where it can be analyzed adequately. Commonly used tools for data ingestion are Apache Kafka, Apache Storm, Apache Flume, Apache Wavefront, Sqoop, and more.
- Data buffering tools: Data buffering is important where streaming data is increasingly spawned from thousands of data sources. Commonly used tools for data buffering are Redis Cache, Kinesis, GCP Pub/Sub, and so forth.
Data engineering is an advanced profession and in-demand job among the big data domain. So, if you are planning to get into Data Engineering, equip yourself with the functional knowledge of important tools like Apache spark.
Automation Skills
Data Engineers solve complex problems to provide accurate, high-quality, and complete data to all stakeholders in an easily accessible manner.
Data Engineering benefits from automating tasks in six key areas:
- Data Collection: Reliability, accuracy, and coherence of data are indicators of effective data collection. Automation supports the process, ensuring event-driven data is delivered persistently and ETL pipelines are strong and run on a dependable schedule.
- Data Sanitization: Accuracy and consistency indicate successful data sanitization, with data engineers removing private or sensitive information. Automation ensures private data isn't supplied and only appropriate stakeholders have access.
- Data Cleansing: As the most time-consuming process in data engineering, accuracy and consistency are key indicators. Automation inspects records, makes updates, removes unnecessary data, and corrects bad data.
- Data Warehousing: Automation ensures consistency and accuracy in the process, delivering and storing data logically according to schedule.
- Data Integration: Used for activation to deliver data closer to real-time than warehousing, automation ensures connecting data between systems and all tools and teams working with the same data.
- Reverse-ETL: Extending data integration that relies on warehousing, automation ensures data sent via reverse-ETL contains desired insights and faults don't augment negative impacts.
Data engineers have long used automation in various tools to help with these tasks and application testing to meet stringent requirements.
Data Engineers solve complex problems to provide accurate, high-quality, and complete data to all stakeholders, in a way that is easy to access.
Data Engineering benefits of automating tasks in six key areas:
- Data Collection: Reliability, accuracy, and coherence of data are indicators of effective data collection. Automation supports the process, ensuring event-driven data is delivered persistently and ETL pipelines are strong and run on a dependable schedule.
- Data Sanitization: Accuracy and consistency indicate successful data sanitization, with data engineers removing private or sensitive information. Automation ensures private data isn't supplied and only appropriate stakeholders have access.
- Data Cleansing: The most time-consuming process in data engineering, accuracy, and consistency are key indicators. Automation inspects records, makes updates, removes unnecessary data, and corrects bad data.
- Data Warehousing: Automation ensures consistency and accuracy in the process, delivering and storing data logically according to schedule.
- Data Integration: Used for activation to deliver data closer to real-time than warehousing, automation ensures connecting data between systems and all tools and teams working with the same data.
- Reverse-ETL: Extending data integration that relies on warehousing, automation ensures data sent via reverse-ETL contains desired insights and faults don't augment negative impacts.
Data engineers have long used automation in various tools to help with these tasks and application testing to meet the stringent requirements.
Machine Learning and Algorithms
Data engineers need ML skills and knowledge of algorithms to do their job. They focus on creating end-to-end pipelines, ETL tasks, and regression, classification, and clustering.
Faulty data can lead to models learning incorrect patterns, regardless of the modeling techniques used. Data Engineers create the necessary groundwork for scientific research, allowing stakeholders to gather and analyze raw data from multiple sources and formats.
Data engineers need ML skills and knowledge of algorithms to perform their job effectively. They focus on creating end-to-end pipelines, ETL tasks, and working with regression, classification, and clustering techniques.
Faulty data can lead to models learning incorrect patterns, regardless of the modeling techniques used. Data engineers lay the necessary groundwork for scientific research, allowing stakeholders to gather and analyze raw data from multiple sources and formats.
Having a basic understanding of ML concepts is essential for comprehending the needs of data scientists and ML engineers. Data engineering enables ML, data exploration, and other analytical projects involving large datasets. It is important for data engineers to have knowledge of supervised and unsupervised learning, clustering techniques, and the use of the k-means clustering algorithm with Spark MLlib and other big data tools.
Cloud Computing
Data engineering and cloud computing are intertwined, as the rise of Big Data has led organizations to prefer cloud-based solutions such as Hadoop on AWS, Dataproc on Google Cloud, and Azure Databricks for efficient data storage, processing, and handling.
Data is critical for every company, so they often use a hybrid cloud approach, employing prominent cloud platforms for data engineering, such as AWS, Azure, GCP, OpenStack, OpenShift, and more.
Data engineers must be highly skilled in building cloud computing solutions at scale and applying data engineering expertise to develop distributed systems using software engineering best practices for Big Data solutions. They are expected to be proficient in continuous deployment, code validation, logging, instrumentation techniques, and monitoring.
FAQs about Data Engineering Skills
We've got answers to your most frequently asked questions.
How to prepare for a data engineering interview?
Data engineers require mathematical skills, but these are not the core focus. Such skills can be useful in subsequent steps for problem-solving, and regular coding encourages logical thinking. As you gain more data engineering experience, you will increasingly apply logical reasoning to problem-solving and develop innovative solutions, while honing your critical thinking abilities.
What security skills does a data engineer need?
Security is essential in data engineering because applications are often available over networks and connected to the cloud, making them vulnerable to security threats and breaches. Therefore, it is important for data engineers to perform basic security testing as part of the development process to ensure that there are no security vulnerabilities in a new or updated version of an application, thus ensuring compliance with security criteria.
Is data engineering part of data science?
Data science is highly collaborative, with data engineers working with various stakeholders.
Data engineers develop data collection processes, integrate new technologies into existing systems, and streamline systems for data collection and analysis.
- Data Science uses a scientific approach to extract actionable business insights from data for decision-making.
- Data Engineering involves designing, creating, building, and maintaining data pipelines to collect and combine raw data from various sources, ensuring optimization.
Can I get a job with data engineering certification?
Learning from the best is the best way to refresh your knowledge, and building specialized skills is a strong bet. High-quality Data engineering Certification courses can teach you advanced cloud computing and software engineering skills as a starting point for your data engineering career.
TL;DR
Data Engineers analyze problems by breaking them down into components and then combining these pieces to create creative and effective solutions.
The competition has become even more fierce due to the explosive growth of AI-driven technologies. Employers, in particular, are looking for advanced proficiency in software engineering and analytical skills, including machine learning and cloud computing.