5 Data Engineering Skills to Transform Your Career in 2024

Data management is evolving in exciting ways between the widespread adoption of data-led decision-making, en-masse cloud migration and the mainstreaming of artificial intelligence (AI) technologies. Trends such as real-time analytics, modern cloud data warehouses, data lakehouses, data fabric and data mesh further galvanize action and budgets across industries.

As the demand for data-driven solutions increases, so does the demand for data engineers like you, who design, build and maintain the infrastructure that powers various data-powered applications and analytics. But keeping up with new skills and competencies in this ever-evolving landscape may seem overwhelming. 

The sheer volume of tools available in the market and the breakneck pace of change make learning every data management technology unrealistic. To build skills that sustain and thrive amid constant change, you need to acknowledge that you will continuously be learning, experimenting and innovating throughout your career as a data engineer. Just as your data management systems regularly evolve, so must you. Focus on foundational areas to build core skills that will stand the test of time.

Core responsibilities of modern data engineers include:

• Data architecture: Build, design, test, maintain and modify data infrastructure and workflows to turn raw data into an organized, usable and profitable business asset.
• Data integration: Connect, move and transform large structured and unstructured data sets from diverse sources to diverse targets.
• Systems management: Ensure the quality, reliability, security, speed, performance and scalability of data integration systems.

How can you progress your career while optimizing the performance of your current data management systems and leveraging the full potential of advanced solutions? Focus on building a solid foundational skillset rather than pursuing shiny new objects. Here are five top data engineering skills that can help you achieve that for 2024 and beyond:

1. Data Modeling, Architecture and Warehousing

The core of your job as a data engineer is to move, model and manage vast data sets to be business-ready and accessible to data scientists. Today, even a small business continuously generates large amounts of data in diverse formats that must be stored, moved and transformed. Being able to design and optimize your data architecture to support current and future data integration, storage, processing and consumption needs is a significant competency. 

Picking the right tools to build an optimal data integration tech stack is a skill that will help you thrive well into the future. It’s easier said than done to build one that responds to changing business needs and can manage complex business use cases.

Differentiate yourself with a strong understanding of:

• The end-to-end data lifecycle: Understanding data from ingestion to processing to analysis, the logic behind how it works and what business users want from it.
• Data modeling principles: Defining the structure and relationships between data sets to deliver the right business insights.
• Data storage and warehousing frameworks: Identifying the right cloud and on-premises databases for your business use cases.
• Cost and productivity: Calculating the ROI of your data management ecosystem.

2. High Volume Real-Time Cloud Data Processing

Most big data storage, processing and analytics occur on the cloud due to its scale and speed. Understanding cloud storage options, computing capabilities and networking essentials of major cloud technologies, such as Microsoft Azure, Amazon Web Services (AWS) and Google Cloud, is crucial.

Strong big data fundamentals and expertise in big data technology, such as Apache Spark and Kafka, are crucial to understanding streaming and distributed systems capable of handling real-time and batch processing of structured, semi and unstructured data.

It is your job to ingest, store, organize, find, retrieve, prepare and process a high volume of incoming data to produce business-ready insights. You also need to perform various data manipulation operations, such as filtering, sorting, joining, aggregating, pivoting and splitting data sets.

Data transformation processes, such as extract, transform, load (ETL), extract, load, transform (ELT) and reverse ETL, form the bulk of this work. The crux is that data formats, sources and targets are not only expanding but also changing. Each time you add a new application or tool to your tech stack, you need to create new code and pipelines to connect incoming data. But this is time and effort-intensive, without guaranteeing that your pipeline will be stable or serve a long-term purpose.

Differentiate yourself by learning about modern data integration solutions that can stand the test of time. You should aim to be able to design an infrastructure that responds to changing business needs, automates core data integration tasks — like data discovery, mapping, validation, cleansing and enrichment — and does not compromise on efficiency, cost or performance at scale.

Further specialization across the suite of data services major cloud providers offer is an additional career booster. For example, the Microsoft Azure Cloud includes Azure Storage, Microsoft Fabric, Azure HD Insight, Azure Stream Analytics, Azure Data Factory, Azure Synapse Analytics and Azure Databricks. 

3. Data Quality and Governance

Modern data engineers are held accountable for the accuracy, completeness, consistency and timeliness of the data they manage.

Differentiate yourself by learning how systematic audits can help assess and improve data quality, ensure compliance with data governance standards — such as data lineage and metadata management with data catalogs — and mitigate data privacy and security risks.

4. Data Analysis and User-Centric Visualization

Although data analysis is not your domain, your role is to break down large data sets to extract insights that can help data analysts and scientists do their jobs. First, develop a thorough knowledge of data analysis techniques, including descriptive, diagnostic, predictive and prescriptive analytics. When you combine this with developing solid data visualization skills, it can help you collaborate more effectively with business users to access, interpret and communicate data insights.

5. AI and Machine Learning

Data engineering is entering a new phase where AI-powered data integration solutions do much of the heavy lifting. In this context, expectations from data engineers are bound to change.

For instance, proficiency in programming languages such as Python, SQL, NoSQL, Java, Scala, R and LINUX Command has traditionally been a must. However, no-code, low-code and AI-powered data solutions make day-to-day data engineering tasks — such as creating automated ETL workflows and data pipelines, integrations, transformations and analysis of big data sets — easier to execute, irrespective of the structure, format and scale.

Aside from a general awareness of AI and machine learning (ML) concepts and applications, differentiate yourself early in the game by learning how to leverage AI and ML capabilities to automate and optimize complex workflows with intelligent use of pro-code and low or no-code.

Educate yourself on how AI-powered data integration can save time, effort and costs, free you from day-to-day data ops and ad-hoc firefighting, mitigate risks and boost the ROI of your data investments.

The secret to success with modern data engineering lies not in any specific tools or language but in building a deep understanding of the core data engineering areas that impact performance. At the same time, the right tools and solutions can make all the difference in data management outcomes, and your choice of solutions is crucial for your career as well as the company.

In a dynamic environment, you want stability and security. But you also want to be able to experiment and innovate by leveraging new tools and technologies to stay ahead of industry trends. A data integration solution that balances stability with innovation helps you create a concrete data-led competitive advantage for your company.  

AI-powered data integration solutions like Informatica Cloud Data Integration-Free (CDI-Free) offers a cloud-native, serverless platform to meet your stability needs and innovation use cases. Experiment with your hybrid data integration workflows today without worrying about scale-up tomorrow — while maintaining control over not just the AI but also the pace, scope and costs.

To learn more and get started with CDI-Free today, visit informatica.com/free-data-integration.