With the rapid introduction of new technologies, products in data management and analytics, demand for more insights, the role of the data engineer continues to undergo a significant transformation. Traditionally, data engineers were tasked with building and managing data pipelines, helping ensure data quality, and supporting the integration of analytics into businesses. However, the demands of modern data ecosystems have expanded the scope of data engineering to include advanced responsibilities such as cloud infrastructure management, real-time data processing, cost and multi-cloud optimization.
Let’s explore the key differences between traditional and modern data engineers, the challenges they face, and how AI provides data engineers with distinguished engineer expertise helping them to succeed.
Traditional Data Engineering
Data engineers were tasked with transforming raw data into a usable form that meets the needs of data consumers, including data scientists, analysts, and business users. They focused on the timely delivery of data; ensuring it is properly cataloged, governed, and compliant with relevant regulations.
Data engineers play a crucial role in the success of data and analytics (D&A) initiatives. Without them, organizations faced increased costs, deployment delays, and challenges related to data integration, quality, and availability.
Key Responsibilities
Building and Managing Data Pipelines: Traditional data engineers are responsible for the creation and maintenance of data pipelines, ensuring that data flows efficiently from source systems to data warehouses and/or data lakes.
Operationalizing Data Delivery: Data engineers ensure that data delivery is automated and operationalized for production-level deployments, minimizing manual intervention.
Curating Datasets: They also support non-technical users, such as data scientists, by curating datasets and providing the necessary infrastructure for self-service data preparation.
Supporting Analytics Deployments: Traditional data engineers help deploy analytics and data science outputs into business processes and applications, ensuring they are integrated seamlessly.
Skills Required
Working with Diverse Datasets: Traditional data engineers must be adept at parsing and understanding various data formats.
Collaboration with Domain Experts: They must also work closely with data scientists, analysts, and domain experts to ensure data is properly managed.
Data Governance and Security: Ensuring compliance with data governance and security policies is also a critical aspect of traditional data engineers’ role.
Modern Data Engineering
With the popularity of generative AI, large language models (LLMs), AI and machine learning (ML), cloud-native data warehouses or platforms like Snowflake, Databricks, AWS Redshift, Google BigQuery, business intelligence tools, the expectations of data engineers have increased.
Modern data engineers are now responsible for designing, building, and maintaining the infrastructure necessary for collecting, storing, and processing large volumes of data. They work closely with data scientists, analysts, and other stakeholders to ensure that data systems are robust, efficient, cost optimized, and reliable.
Additionally, data engineers are now expected to be data architects, understand cloud-native platforms, cloud service provider implications, pricing models to optimize consumption, and much more.
That means data engineers are now expected to have the skills of distinguished data engineers, of whom there are too few.
Key Expectations of Data Engineers
Data Pipeline Development: Modern data engineers design, develop, and maintain scalable data pipelines for the efficient collection, transformation, and loading (ETL) of data from various sources.
Data Warehousing: Modern data engineers build and manage data warehouses that store both structured and unstructured data, ensuring data is organized and accessible.
Spend Optimization: A critical aspect of the modern data engineer’s role is ensuring that data usage and associated costs are optimized, preventing licensing and spending overruns.
Data Integration: These engineers integrate data from various sources, including APIs, third-party services, and internal databases, ensuring consistency and accuracy.
Data Quality and Governance: Modern data engineers implement data quality checks and governance policies to maintain data integrity, security, and compliance.
Collaboration: They also work closely with data scientists, analysts, and business stakeholders to understand data needs and provide the necessary infrastructure and tools.
Performance Optimization: Modern data engineers are experts in troubleshooting and optimizing data systems for performance, reliability, and scalability.
Multi-Cloud Expertise: Knowledge of popular cloud platforms and their interworking is essential for modern data engineers.
Documentation: They document data processes, architecture, and infrastructure to ensure transparency and maintainability.
Challenges Faced by Modern Data Engineers
The expanded role of the modern data engineer comes with its own set of challenges:
Managing Complex Data Ecosystems: With the proliferation of data sources and the need for real-time processing, modern data engineers must manage increasingly complex data ecosystems.
Ensuring Data Quality at Scale: As data volumes grow, maintaining data quality and governance becomes more challenging, requiring advanced tools and techniques.
Balancing Performance and Cost: Modern data engineers must optimize data systems for performance while keeping costs under control – especially in cloud environments.
Keeping Up with Technological Advancements: The rapid pace of innovation in data technologies means that modern data engineers must continuously update their skills and knowledge.
Collaboration Across Teams: Modern data engineers must work closely with various stakeholders, including data scientists, analysts, and business users, to ensure that data systems meet organizational needs.
Firefighting: While data engineers need to build data products and pipelines, and ensure quality, they are invariably involved in routine and preventable operational and tactical tasks impacting outcomes.
We Must Help Modern Data Engineers Now
To support modern data engineers in their expanded roles, organizations need to invest in the following areas:
Proper Tooling: Modern data engineers should have access to the latest tools for data integration, quality assurance, and performance optimization. This is crucial.
Continuous Learning and Development: Organizations should invest in ongoing education and skill development of their data engineers to keep them up-to-date with the latest technologies.
Collaboration Platforms: Implementing collaboration platforms that facilitate communication between data engineers, data scientists, and business stakeholders can help streamline processes.
Scalable Infrastructure: Modern data engineers need access to scalable cloud infrastructure that can handle large volumes of data and complex processing requirements.
Automation: Automating routine tasks such as data pipeline monitoring and maintenance can free modern data engineers to focus on more strategic initiatives.
Generative AI: While still gaining acceptance, AI adoption can help augment data engineers.
The Path to Becoming a Distinguished Data Engineer is with AI
GenAI, LLMs, AI- and ML-based data solutions have evolved and can enhance the productivity, efficiency, and impact of data engineers, elevating them to a new level of expertise and influence in modern data ecosystems. Here’s how:
1. Automation of Routine and Repetitive Tasks
One of the biggest challenges data engineers face is managing repetitive tasks, such as setting up data pipelines, cleaning data, ensuring compliance, and monitoring data quality. AI helps data engineers with automated ETL, ELT and ETLT processes, automated data cleansing, monitoring and alerting today.
2. Improved Data Quality and Governance
Ensuring high data quality and enforcing data governance are critical yet time-consuming responsibilities of a data engineer. Today, AI helps with data quality audits, natural language data queries, data governance, and automation
3. Optimizing Cloud Costs and Performance
One of the key expectations of a modern data engineer is the ability to manage cloud resources efficiently and optimize costs. AI helps with predictive scaling, cost analysis and recommendations, and performance optimization.
4. Enhancing Data Pipeline Development
AI can transform the way data engineers build and maintain data pipelines by serving as intelligent assistants for code generation, pipeline debugging and optimization, and automated documentation.
5. Advanced Data Integration and Real-Time Processing
Modern data engineers are tasked with integrating data from various sources, such as APIs, IoT devices, or third-party platforms. AI can simplify this process with AI-driven integration, real-time processing, and data harmonization
6. Collaboration and Communication with Stakeholders
A key aspect of a distinguished data engineer is the ability to communicate effectively with data scientists, analysts, and business stakeholders. AI can enhance this with natural language interfaces and automated insights and reports.
7. Continuous Learning and Skill Development
Given the rapidly evolving data landscape, keeping up with new tools and technologies is essential for data engineers. AI supports continuous learning with personalized learning curriculum, mentorship, and assistance. It just has to be put in place!
Conclusion: The Path to gain Distinguished Engineer expertise is with AI
Modern AI solutions - for example, solutions like Revefi’s “Raden AI Data Engineer” – provide data engineers with tools to automate tedious tasks, optimize performance, improve communication, and enhance their technical capabilities. By leveraging Raden, data engineers can expand their skills, knowledge and responsibilities, becoming distinguished data engineers. Incorporating AI-driven solutions will allow data engineers to focus on strategic initiatives, data architecture, and innovation, setting them apart from their peers in the fast-paced world of data management.
