If you’re looking for a rewarding career in a fast-growing field, becoming a Data Engineer could be the perfect fit.
Today’s businesses are constantly on the hunt for ways to effectively collect, manage, and analyze the massive quantities of data we generate every day. A good Data Engineer can help them do just that, laying the groundwork for organizations to turn heaps of raw data from multiple sources into valuable, actionable insights that will guide their decisions.
And as more businesses begin to appreciate the massive potential of their data, demand for professionals to handle it is on the rise. According to the U.S. Bureau of Labor Statistics, the growth rate for data engineering jobs sits at 8%—faster than the 3% rise for other jobs. Due to the demand for their valuable skills, experienced Data Engineers earn around $125,000 on average in the U.S., making it a potentially lucrative career with plenty of scope for development.
Though it might sound like a technical role, it’s possible to cross-train into data engineering from other roles. All you need is the right skills on your resume and a bit of hands-on experience with some of the field’s leading data management systems.
Let’s dive into the skills, certifications, and know-how you really need to become a great Data Engineer (and the best way to go about it).
What does a Data Engineer do?
- On a day-to-day basis, that might involve:
- Designing and building systems that collect, store, and analyze data
- Using database management systems like MySQL, Oracle, PostgreSQL, and MongoDB, and cloud-based warehouse technologies such as BigQuery, Firebolt, and Amazon Redshift
- Building data pipelines to bring together information from different source systems
- Writing algorithms to clean and standardize data
- Managing and storing data securely to protect it from loss or theft
- Developing business intelligence reports that can be reused
- Ensuring compliance with data governance and security policies
Best certifications for Data Engineers
Certifications are a great way to guide your learning, showcase your knowledge, and prove your skills to potential employers. There are tons of certifications out there for budding Data Engineers.
In the longer term, which ones are most appropriate for your career journey will depend on what areas you decide to specialize in and what kind of products are most popular in your chosen niche or industry.
You’ll find plenty of useful certifications from data solution vendors once you’ve decided which products you want to work with. But if you’re just getting started as a Data Engineer, here are a few foundational data and AI-related certifications you could look into to give you a broad understanding of data engineering techniques and help you get your foot in the door.
Technical skills for Data Engineers
Programming languages
- Python: The most popular language for data science and machine learning (ML), Python offers a vast ecosystem of libraries for data manipulation, analysis, and visualization.
- SQL: Essential for working with relational databases, querying data, and performing complex data transformations.
- Java/Scala: Used in big data frameworks like Hadoop and Spark, built especially for large-scale data processing.
Big data technologies
- Hadoop: A framework for storing and processing massive datasets across a cluster of computers.
- Spark: A fast and general-purpose cluster computing system that can be used for various tasks, including data processing, ML, and stream processing.
- Kafka: A distributed streaming platform for handling real-time data streams.
Cloud computing
As a Data Engineer, you’ll be using cloud-based tools to store data, deploy data pipelines, and build and manage data infrastructure.
Many businesses now take a multi-cloud approach to their cloud computing stack, using more than one cloud service provider at a time. This helps businesses access a wider range of services and build more resilience by not putting all their metaphorical eggs in one basket.
For this reason, it’s useful for Data Engineers to get familiar with more than one of the big cloud service providers (CSP). The good news is that the most popular CSPs offer limited access to many of their services for free, so you can experiment with their products. Look out for data storage, analytics, and ML services and give them a spin.
Data storage and handling
Architecture types
- Relational databases: Structured tables with rows and columns to organize data, enabling efficient querying and data integrity.
- NoSQL databases: Designed for flexible data models, handling unstructured or semi-structured data, and scaling horizontally.
- Data warehouses: Centralized repositories designed to handle analytical queries, providing a single source of information for business intelligence.
- Data lakes: Store data in its raw format, allowing for a wide range of analytical and operational use cases.
- Distributed file systems: Enable scalable storage and processing of massive datasets across a cluster of computers, for instance, the Hadoop Distributed File System.
Data warehousing processes
- ETL (Extract, Transform, Load): This is the core process of data warehousing. It involves extracting data from various sources (like databases, APIs, and files), transforming it into a consistent format suitable for analysis, and loading it into the data warehouse.
- Data modeling: Designing the structure within the data warehouse using common approaches like dimensional modeling, which organizes data into facts (measurements) and dimensions (attributes that provide context to the facts).
- Data QA: Ensuring data accuracy, completeness, consistency, and timeliness by implementing data validation rules, data profiling, and data cleansing techniques.
- Metadata management: Tracking information about the data, such as its source, lineage, and quality, to help users understand and trust the data.
Machine learning (ML)
Today, many businesses are harnessing their data to take advantage of ML. Data is the fuel that powers ML; ML models learn from data, identifying patterns and relationships that enable them to make predictions or decisions.
Since a lot of your work as a Data Engineer will be carried out with ML in mind, you need to understand the concepts involved and the impact your data wrangling can have on ML process. Here are some things to brush up on.
- Types of ML, including supervised, unsupervised, and reinforcement learning.
- Common algorithms like decision trees, linear regression, support vector machines, clustering algorithms, and neural networks.
- Data preprocessing techniques like cleaning, transforming, and preparing data for ML models.
- Model evaluation metrics like accuracy, precision, recall, and F1-score.
- Model deployment and monitoring, from deploying models into production environments to monitoring their performance over time.
- ML libraries including options like scikit-learn, TensorFlow, and PyTorch.
Data engineering tools
- Apache Spark: A powerful engine for large-scale data processing, supporting various operations like ETL, ML, and stream processing.
- Apache Kafka: A high-throughput messaging system for handling real-time data streams, crucial for building data pipelines that react to events in real time.
- Apache Airflow: A platform for defining, scheduling, and monitoring data pipelines, ensuring that data flows smoothly and reliably.
- dbt (data build tool): Enables Data Analysts and Engineers to transform data within their data warehouses using SQL, improving code maintainability and collaboration.
- Git: Essential for version control, allowing Data Engineers to track changes to their code, collaborate effectively, and easily revert to previous versions if needed.
Other key data skills Data Engineers should develop
- Data visualization: The ability to create insightful, engaging visualizations to help tell stories with data by using tools like Tableau or Power BI.
- Data security: Understanding of data security best practices to protect sensitive data.
- Data privacy: Familiarity with all applicable laws and regulations around data handling in your industry and region so you can remain compliant at all times.
Soft and consultancy skills for Data Engineers
It’s not just technical expertise in data handling that makes a great Data Engineer. A successful data professional will also have strong soft skills that help them work effectively and function well within a team.
These soft skills are crucial for Data Engineers because of how often they need to collaborate with cross-functional teams, including both technical and non-technical stakeholders. Effective communication, problem-solving, and teamwork are essential for understanding business requirements, translating them into technical solutions, and delivering successful projects.
- Communication: You’ll need to be able to explain complex technical concepts to both technical and non-technical audiences, understand the needs and requirements of stakeholders by actively listening, and create well-documented code, reports, and presentations.
- Collaboration: You’ll have to work effectively with Data Scientists, Analysts, and other team members, and be able to build and maintain strong relationships with stakeholders.
- Problem-solving: Analytical thinking will be critical to identify and diagnose data quality issues and system bottlenecks. You’ll also need to think creatively and develop innovative solutions to complex data challenges.
- Adaptability: Your organization's requirements and the technologies at your fingertips will constantly shift, so you’ll need to be able to adjust to changing priorities and requirements and stay up-to-date with the latest technologies and trends in the field.
- Business acumen: Understanding business goals and how data can drive business value will help you create effective solutions that align with commercial objectives.
- Project management: You’ll need to be able to manage your data engineering projects effectively from beginning to end, including planning, execution, and delivery.
How to gain the skills you need to be a Data Engineer
Ready to start your career in data engineering? Revolent can help you learn all the skills we’ve talked about—and land your first role—through our fully paid, supported training and placement program.
As a recognized training provider of several leading data engineering platforms, we’ll help you cross-train as an in-demand Data Engineer and provide a paid work placement with a market-leading company.
And the best part is, you don’t need any prior experience with cloud platforms or data to be eligible.
