Data Engineering Skills and Toolbox for 2024

Team Contributor: Data Engineer George Michael Dagogo

Understand these concepts and how to implement them and you’ll always be in demand in the Data Engineering field. With numerous new tools coming out left and right it is important to grasp the logic behind a skill, by doing this you can easily implement any new tool you encounter.

  1. Database Management
  2. Data warehousing
  3. Programming
  4. Data Transformation/Processing
  5. ETL / ELT
  6. Cloud Computing
  7. Automation/Scheduling
  8. Problem-Solving and Analytical Thinking

1. Database management

refers to the process of organizing, storing, and maintaining large amounts of data in a structured manner within a database system. It involves the design, creation, implementation, and administration of databases to ensure data integrity, security, and efficient access.

Database management is essential for data engineers due to the following reasons:

  • Data Storage and Organization: Data engineers are responsible for designing and implementing the data storage infrastructure. They need to understand the principles of database management to determine the appropriate database systems and structures to efficiently store and organize data. By implementing effective database management practices, data engineers ensure that data is stored in a structured and scalable manner.
  • Data Integration: Data engineers often work with multiple data sources, such as databases, data warehouses, and external systems. Database management skills are crucial for integrating and consolidating data from various sources into a unified database. This involves understanding data schemas, handling data transformations, and ensuring data consistency and quality during the integration process.
  • Data Quality and Integrity: Database management involves enforcing data integrity constraints and maintaining data quality. Data engineers need to understand these principles to ensure the accuracy and reliability of the data being processed and stored. They implement data validation checks, perform data cleansing, and establish data quality standards to maintain the integrity of the database.
  • Performance Optimization: Efficient data access and retrieval are critical for data engineers working on large-scale data processing and analytics tasks. Knowledge of database management allows data engineers to optimize queries, design appropriate indexes, and fine-tune the database system to enhance query performance and overall system efficiency.
  • Data Security: Data engineers are responsible for implementing data security measures to protect sensitive information. Database management skills help them understand security mechanisms, implement access controls, and ensure data encryption and auditing to safeguard data against unauthorized access or breaches.
  • Data Modeling: Data modeling is the process of creating a conceptual representation of the database structure, often using diagrams such as entity-relationship (ER) diagrams. It helps to understand and communicate the relationships between different data entities.
  • Collaboration with Database Administrators: Data engineers often collaborate with database administrators (DBAs) to manage the database infrastructure. Having a strong understanding of database management enables effective communication and collaboration with DBAs, facilitating smoother coordination in tasks such as database design, performance optimization, and troubleshooting.

In summary, database management is crucial for data engineers as it enables them to design and implement efficient data storage structures, integrate and process data effectively, ensure data quality and integrity, optimize performance and collaborate with other stakeholders involved in database management. These skills are fundamental for data engineers to handle data effectively and deliver successful data engineering projects.

Tools: SQL(MySQL is good for beginners) then MySQL workbench, you can learn the rest as you keep moving up in your career.

2. Data warehousing

is a process of collecting, organizing, and managing large volumes of data from various sources within an organization. It involves the extraction, transformation, and loading (ETL) of data from different operational systems into a central repository known as a data warehouse.

A data warehouse is designed to support the analysis and reporting of data to facilitate business decision-making. It integrates data from multiple sources, such as transactional databases, spreadsheets, flat files, and external systems, and stores it in a structured format optimized for querying and analysis.

The main goals of data warehousing are:

  • Data Consolidation: Data from different sources are consolidated into a single, unified view. This eliminates data silos and provides a holistic view of the organization’s operations.
  • Data Integration: Data is transformed and standardized during the ETL process to ensure consistency and quality. This involves cleaning, filtering, and aggregating the data to make it suitable for analysis.
  • Historical Data Storage: Data warehousing stores historical data, allowing for analysis and trend identification over time. This enables organizations to perform comparative analysis, identify patterns, and make data-driven decisions based on historical performance.
  • Business Intelligence and Reporting: Data warehouses provide a platform for business intelligence tools and reporting systems to access and analyze data. Users can run complex queries, generate reports, and perform data mining to gain insights into business operations.
  • Decision Support: By providing a centralized and reliable source of data, data warehousing supports decision-making processes across various levels of an organization. Executives, managers, and analysts can access the data warehouse to retrieve relevant information and make informed decisions.

Overall, data warehousing plays a crucial role in enabling organizations to leverage their data assets effectively, gain insights, and make strategic business decisions based on reliable and comprehensive information.

Tools: Google BigQuery, AWS Redshift, AZURE Synapse. Learn one of these and the rest can be implemented easily.

3. Programming

is the process of creating computer programs by writing instructions that a computer can understand and execute. It involves designing, coding, testing, and maintaining sets of instructions or algorithms that direct the behavior of a computer system or software application.

Data engineers need programming skills for several reasons:

  • Data Manipulation and Transformation: Programming allows data engineers to manipulate, process, and transform data efficiently. They can write code to clean, filter, aggregate, and reshape data according to specific requirements. Programming languages provide libraries and frameworks that offer powerful data manipulation and analysis capabilities, making it easier to handle large volumes of data and perform complex transformations.
  • ETL (Extract, Transform, Load) Processes: Data engineers are responsible for building and maintaining ETL processes, which involve extracting data from various sources, transforming it into a suitable format, and loading it into a target system or data warehouse. Programming enables them to write scripts or programs that automate these processes, ensuring data flows smoothly and accurately.
  • Integration of Data Sources: Data engineers often work with diverse data sources, including databases, APIs, log files, and streaming data. Programming skills allow them to connect to these sources, retrieve data programmatically, and integrate it into a unified system. They can utilize programming languages to write connectors, data extraction scripts, and data ingestion pipelines.
  • Scalability and Performance: Large-scale data processing requires efficient and scalable solutions. Programming enables data engineers to optimize data processing algorithms, implement parallel processing, and leverage distributed computing frameworks. They can write code that takes advantage of multi-core processors, clusters, or cloud computing platforms to handle big data workloads effectively.
  • Automation and Workflow Orchestration: Programming allows data engineers to automate repetitive tasks and create workflows for data processing and integration. They can use programming languages to build data pipelines, schedule jobs, and orchestrate the flow of data through different stages of processing. Automation reduces manual effort, increases productivity, and ensures consistent data processing.
  • Custom Tooling and Infrastructure: Data engineers often need to develop custom tools, scripts, or applications to address specific data engineering needs. Programming skills enable them to build tailored solutions for data validation, monitoring, data quality checks, or performance optimization. They can create APIs, user interfaces, or command-line tools that facilitate data engineering tasks.
  • Collaboration with Data Scientists and Analysts: Data engineers often collaborate with data scientists and analysts who use programming languages like Python or R for data analysis and modeling. Proficient programming skills allow data engineers to understand and work effectively with the output of data scientists’ work, integrate their models into production systems, and provide the necessary data infrastructure to support their analysis.
  • Troubleshooting and Debugging: Programming skills are valuable for troubleshooting data engineering processes and identifying issues. Data engineers can write diagnostic scripts, analyze logs, and debug code to resolve errors or bottlenecks in data pipelines. Strong programming skills enable them to identify and fix problems efficiently.

In summary, programming is an essential skill for data engineers as it empowers them to manipulate data, build scalable data processing systems, automate workflows, integrate diverse data sources, and collaborate effectively with other data professionals. Programming proficiency enables data engineers to design efficient data pipelines, optimize performance, and ensure the reliable and timely delivery of high-quality data.

Tools: Python and SQL can take you far in your career as most tools that require coding will be based on these two then later in your career you can pick up Scala, Java or GO.

4. Data Transformation/Processing

refers to the manipulation and modification of data to convert it from its raw form into a more structured and useful format for analysis, reporting, and other purposes. It involves applying various operations and techniques to cleanse, filter, aggregate, merge, and derive insights from the data.

Data Transformation/Processing is an essential skill for data engineers due to several reasons:

  • Data Integration: Data engineers often work with diverse data sources that may have different formats, structures, or schemas. Data transformation allows them to integrate and merge these disparate data sources into a unified and consistent format. It enables the creation of a single source of truth for data analysis and reporting.
  • Data Quality and Consistency: Data transformation involves cleaning and standardizing data, which helps ensure data quality and consistency. By identifying and correcting errors, handling missing values, and standardizing formats, data engineers can improve the reliability and accuracy of the data.
  • Data Preparation for Analysis: Before data can be analyzed effectively, it often needs to be transformed and preprocessed. Data engineers transform data to make it suitable for downstream processes such as machine learning, statistical analysis, or data visualization. By performing tasks like data aggregation, normalization, or feature engineering, they enable efficient and accurate analysis.
  • Performance Optimization: Data transformation can improve the performance of data processing and analysis tasks. By aggregating and summarizing data, eliminating unnecessary columns, or optimizing data structures, data engineers can enhance the efficiency and speed of data operations. This is particularly important when dealing with large volumes of data or when working with real-time data processing systems.
  • Automation and Reproducibility: Data transformation workflows can be automated and reproducible, which is crucial for maintaining data pipelines in a production environment. Data engineers can create data transformation pipelines using frameworks like Apache Airflow or workflow automation tools. Automation ensures consistent data processing and reduces the risk of manual errors.

In summary, data transformation/processing is a fundamental skill for data engineers as it enables them to integrate, clean, preprocess, and prepare data for analysis, ensuring data quality, consistency, and performance. It forms the foundation for effective data engineering and supports data-driven decision-making within organizations.

Tools: Depending on the size of the data you should learn Pandas first then Apache Spark for large volumes of data. There are cloud tools that are low-code to no-code like AZURE data factory or AWS Data Pipeline.


ELT (Extract, Load, Transform) and ETL (Extract, Transform, Load) are two approaches to data integration and processing, with slight differences in the order of operations. Both methods are used to extract data from various sources, transform it into a desired format, and load it into a target system or data warehouse. Let’s explore each approach:

ETL (Extract, Transform, Load):
1. Extract: In the ETL process, data is first extracted from multiple sources, which can include databases, files, APIs, or other systems. The extraction process involves identifying the relevant data to be collected and pulling it from the source systems into a staging area.

2. Transform: Once the data is extracted, it goes through a transformation phase. In this step, data engineers apply a series of rules, business logic, and transformations to clean, filter, aggregate, and convert the data into a consistent and usable format. This includes tasks such as data cleansing, data validation, deduplication, data enrichment, and joining multiple data sources.

3. Load: The transformed data is then loaded into a target system or data warehouse, where it can be stored and accessed for reporting, analysis, or other purposes. Loading may involve creating tables or schemas in a database, mapping the transformed data to the target structure, and inserting or updating the data.

ETL is a batch-oriented process that traditionally operates in a structured and scheduled manner. It often requires a dedicated ETL server or middleware to manage and orchestrate the extraction, transformation, and loading steps.

ELT (Extract, Load, Transform):
1. Extract: Similar to ETL, ELT starts with extracting data from various sources. The extraction process gathers data from source systems and loads it into a target storage system, such as a data lake or data warehouse. In ELT, the extracted data is typically stored in its raw or near-raw form, without significant transformations.

2. Load: After extraction, the data is loaded into a target storage system, maintaining its original structure and format. This allows for the efficient and scalable storage of large volumes of raw data. The load phase focuses on data ingestion, ensuring that the extracted data is organized and accessible within the target system.

3. Transform: The transformation phase occurs after the data is loaded into the target storage system. In ELT, data transformations are performed directly within the target environment. This can involve using distributed processing frameworks (e.g., Apache Spark) or query languages (e.g., SQL) to process and transform the data on demand. ELT allows for flexible, ad-hoc transformations and exploratory analysis, as the raw data is readily available for transformation and analysis within the target system.

ELT leverages the power and scalability of modern data storage and processing technologies, enabling organizations to store and process vast amounts of data efficiently. It allows data analysts and data scientists to perform complex transformations and analysis directly on the raw data without upfront processing.

The choice between ETL and ELT depends on factors such as data volume, data complexity, desired agility, and the organization’s data processing requirements. ETL is typically used when extensive transformations are needed before loading the data, while ELT is suitable when raw data storage and flexible transformations are desired for exploratory analysis and advanced processing.

6. Cloud Computing

for data engineers refer to the utilization of cloud-based infrastructure, platforms, and services to perform data engineering tasks and processes. It involves leveraging the resources and capabilities provided by cloud service providers to store, process, analyze, and manage data efficiently.

Data engineers need cloud computing knowledge for several reasons:

1. Scalability and Elasticity: Cloud computing provides data engineers with access to scalable and elastic resources. They can leverage cloud services to provision and scale computing resources, storage, and databases based on the requirements of data processing tasks. This scalability allows data engineers to handle large volumes of data efficiently and accommodate fluctuations in data processing demands.

2. Cost Efficiency: Cloud computing offers cost-effective solutions for data engineering tasks. Instead of investing in expensive on-premises infrastructure, data engineers can utilize cloud services on a pay-as-you-go basis. They can provision resources as needed and only pay for the actual usage, avoiding upfront capital expenses and minimizing operational costs.

3. Storage and Data Management: Cloud providers offer various storage solutions, such as object storage, file storage, and database services. Data engineers need cloud computing knowledge to understand these storage options and select the appropriate one for storing and managing their data. They can leverage cloud storage services to store, organize, and retrieve data efficiently, taking advantage of the scalability and durability offered by cloud providers.

4. Data Processing and Analytics: Cloud computing platforms provide powerful tools and services for data processing and analytics. Data engineers can utilize distributed computing frameworks, such as Apache Spark or Hadoop, available on cloud platforms to process large datasets in parallel. They can also leverage cloud-based data analytics services and tools to perform advanced analytics, build machine learning models, and gain insights from the data.

5. Data Pipelines and Orchestration: Cloud computing platforms offer services for building and orchestrating data pipelines. Data engineers can utilize tools like Apache Airflow or cloud-native solutions like AWS Glue or Azure Data Factory to create, schedule, and manage data pipelines. These tools enable the automation and coordination of data processing tasks, ensuring the smooth flow of data through various stages of transformation and loading.

6. Integration and Connectivity: Cloud computing platforms provide integration capabilities that allow data engineers to connect and integrate various data sources and systems. They can utilize cloud-based APIs, connectors, and services to extract data from on-premises systems, third-party applications, or external data sources. Cloud-based integration services facilitate the seamless movement and transformation of data between different systems.

7. Collaboration and Data Sharing: Cloud computing promotes collaboration and data sharing among data engineers and other stakeholders. Data engineers can leverage cloud-based collaboration tools, version control systems, and shared storage to work collaboratively on data engineering projects. They can also share data, insights, and reports with other team members or stakeholders securely and easily.

8. Disaster Recovery and High Availability: Cloud computing platforms offer built-in disaster recovery and high availability features. Data engineers can design data engineering solutions that are resilient to failures and ensure the availability of critical data processing systems. Cloud providers often have redundant infrastructure and automatic backup mechanisms that help protect data and ensure business continuity.

In summary, cloud computing knowledge is essential for data engineers to leverage the scalability, cost efficiency, storage options, data processing capabilities, and collaboration features provided by cloud platforms. It allows them to build robust and scalable data engineering solutions, process and analyze large volumes of data efficiently, and collaborate effectively with other team members. Cloud computing has become a fundamental aspect of modern data engineering practices, enabling data engineers to leverage the power of the cloud to handle complex data processing tasks.

Tools: Learn about the structure of cloud computing then learn about the services Data Engineers use in one of these Cloud service providers AWSAZURE or GCP.

7. Automation/Scheduling

refers to the process of using tools, scripts, or workflows to automatically perform repetitive or manual tasks involved in data engineering processes. It involves streamlining and optimizing data-related operations by eliminating manual intervention and reducing human error. Automation plays a crucial role in increasing efficiency, scalability, and reliability in data engineering workflows.

Automation and scheduling are important for data engineers for several reasons:

1. Efficiency and Productivity: Automation eliminates the need for manual intervention in repetitive and time-consuming tasks, allowing data engineers to focus on more critical and complex aspects of their work. By automating routine processes, data engineers can accomplish tasks more quickly and efficiently, increasing overall productivity.

2. Error Reduction: Automation minimizes the risk of human error that can occur during manual data processing tasks. By automating data pipelines, transformations, and loading processes, data engineers can reduce the chances of data inconsistencies, inaccuracies, or other errors that can negatively impact data quality and analysis.

3. Scalability: Automation enables data engineers to handle large volumes of data and scale their operations effectively. With automated workflows, data engineers can easily accommodate increasing data volumes, adjust processing schedules, and scale computing resources to meet the demands of growing data needs.

4. Timeliness and Reliability: Scheduling allows data engineers to execute data processing tasks at predetermined intervals or specific times. By automating schedules, data engineers ensure that data pipelines run on time, enabling timely availability of updated data for analysis, reporting, and decision-making processes.

5. Continuous Data Processing: Automation and scheduling enable continuous data processing, allowing data engineers to process data in real-time or near-real-time. This is particularly important in scenarios where data needs to be ingested, transformed, and loaded into target systems promptly to support real-time analytics or operational decision-making.

6. Resource Optimization: Automation and scheduling help optimize resource utilization. Data engineers can schedule tasks to run during off-peak hours, distribute workloads across computing resources efficiently, and scale resources up or down based on demand. This allows for better resource management, cost optimization, and improved overall system performance.

7. Monitoring and Alerting: Automation facilitates the monitoring of data pipelines and processes. Data engineers can set up automated monitoring mechanisms to track the progress, performance, and health of data workflows. They can also configure alerts and notifications to be alerted of any issues, failures, or performance bottlenecks, allowing them to take proactive actions and minimize downtime.

8. Reproducibility and Auditability: Automation promotes reproducibility and auditability in data engineering processes. By having automated workflows and processes documented, data engineers can easily reproduce previous runs, track changes, and ensure consistent outcomes. This is crucial for compliance, troubleshooting, and maintaining data lineage.

Overall, automation and scheduling enable data engineers to streamline their work, improve efficiency, reduce errors, and ensure timely and reliable data processing. These practices enhance the overall quality of data engineering processes, support data-driven decision-making, and contribute to the success of data-driven initiatives within organizations.

Tools: Cron, Prefect, Airflow but it’d be advisable to learn Cron first.

8. Problem-Solving and Analytical Thinking

Arguably the most important skill in all of tech, Problem-solving and analytical thinking are cognitive skills that involve the ability to identify, analyze, and solve problems or challenges by applying logical reasoning, critical thinking, and data-driven approaches.

Data engineers need strong problem-solving and analytical thinking skills for several reasons:

  • Data Pipeline Design and Optimization: Data engineers are responsible for designing and optimizing data pipelines to ensure efficient and accurate data processing. They need problem-solving skills to identify bottlenecks, optimize performance, and resolve issues that may arise during data ingestion, transformation, and loading processes.
  • Data Quality Assurance: Data engineers play a crucial role in ensuring data quality. They need analytical thinking skills to analyze data quality issues, identify patterns, and develop strategies to improve data accuracy, completeness, and consistency. Problem-solving skills help them identify the root causes of data quality problems and implement effective solutions.
  • Troubleshooting and Debugging: Inevitably, data engineering processes encounter issues, such as data inconsistencies, job failures, or performance problems. Data engineers with strong problem-solving skills can effectively troubleshoot and debug these issues, identifying the underlying causes and implementing solutions to resolve them promptly.
  • Performance Optimization: Data engineering often involves dealing with large volumes of data and complex transformations. Data engineers need analytical thinking skills to analyze performance metrics, identify areas for improvement, and optimize data processing workflows. They can use problem-solving skills to fine-tune algorithms, optimize queries, and leverage caching mechanisms for better performance.
  • System Architecture and Scalability: Data engineers need analytical thinking skills to design scalable and reliable data systems. They analyze requirements, evaluate system architectures, and make decisions regarding technologies, frameworks, and infrastructure. Problem-solving skills help them anticipate potential challenges, evaluate trade-offs, and develop effective solutions for scaling data infrastructure.
  • Data Security and Privacy: Data engineers need to consider security and privacy requirements when handling sensitive data. Analytical thinking helps them assess potential vulnerabilities, identify risks, and develop strategies to safeguard data. Problem-solving skills are essential for implementing data encryption, access controls, and data anonymization techniques.
  • Collaboration and Stakeholder Management: Data engineers often collaborate with other teams, stakeholders, or data consumers. Analytical thinking helps them understand and analyze stakeholders’ requirements, identify pain points, and propose data solutions that align with business needs. Problem-solving skills enable effective communication, negotiation, and collaboration with diverse stakeholders.

By leveraging problem-solving and analytical thinking skills, data engineers can navigate complex data engineering challenges, develop robust solutions, optimize data processes, and deliver high-quality data solutions. These skills empower them to address data-related issues proactively, make informed decisions, and contribute to the success of data-driven initiatives within organizations.


It is usually advised to transition to Data Engineering from other roles like Data Analytics, Software Engineering, or Cloud Engineering as one of the overlooked reasons is there is a competency factor when choosing a Data Engineer because mistakes made in data transformation or architecture cannot be easily fixed especially when company decisions have been based on the prepared data.

You don’t have to learn everything listed above, if you are good in Python, SQL, and one of the cloud service providers you can start applying for entry-level jobs(which are kind of hard to get these days) but if you want to stand out and be valuable then you will need them.

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