Data Engineering plays a crucial role within the AWS Cloud ecosystem, offering essential data solutions to end-users. The AWS Data Engineer play a big role by facilitating the management of Data Pipelines, Data Transfers, and Data Storage, all within the Amazon Web Services cloud platform. To excel in Data Engineering on AWS, a solid grasp of AWS and foundational data engineering principles is essential. For those seeking to cultivate their Data Engineering skills from the ground up, pursuing the AWS Data Engineer Certification is highly recommended.
For newcomers to the field of Data Engineering, enrolling in the AWS Data Engineer Certification Beta course is an excellent choice. This certification, known as AWS Certified Data Engineer Associate (DEA-C01), marks the fourth Associate-level certification provided by AWS, standing alongside the Solutions Architect, Developer, and SysOps Administrator Associate exams.
What is the New AWS Certified Data Engineer – Associate Exam?
The AWS Certified Data Engineer – Associate certification validates your expertise in essential AWS data services. It demonstrates your ability to construct data pipelines, effectively manage monitoring and troubleshooting, and optimize cost and performance, all while adhering to industry best practices.
If you are eager to leverage AWS technology to transform data into valuable insights for analysis, this beta examination offers a unique opportunity to be among the trailblazers in attaining this newly introduced certification.
Who should take the exam?
As per the DEA-C01 exam guide released by AWS, the AWS Certified Data Engineer – Associate (DEA-C01) exam is designed for individuals with 2-3 years of experience in AWS data engineering and at least 1-2 years of hands-on experience with AWS services.
AWS also emphasizes that candidates should possess expertise in managing the challenges posed by data volume, diversity, and velocity, encompassing tasks such as data ingestion, transformation, modeling, security, governance, privacy, schema design, and the creation of optimal data storage solutions.
The AWS Certified Data Engineer 2023 Exam has announced its dates, with testing taking place from November 27, 2023, to January 12, 2024. The AWS Certified Data Engineer – Associate (DEA-C01) exam is currently in its beta phase, and you can register for the beta version of this examination, commencing on October 31, 2023.
Exam Domains
The AWS Data Engineer Associate Certification Exam comprises four distinct domains. Let’s explore each of these four domains covered in the DEA-C01 exam in greater detail:
Domain 1: Understanding Data Ingestion and Transformation (34%)
This domain constitutes over a third of the total exam content and focuses on processes related to data ingestion, transformation, and management, along with orchestrating ETL (Extract, Transform, Load) pipelines for data handling. It necessitates familiarity with AWS services like Kinesis, Redshift, and DynamoDB streams, as well as the ability to transform data according to specific requirements using tools such as Lambda, EventBridge, and AWS Glue workflows.
Furthermore, a solid grasp of fundamental programming concepts, including infrastructure as code, SQL query optimization, and CI/CD (Continuous Integration and Continuous Delivery) for pipeline testing and deployment, is crucial.
Domain 2: Understanding Data Store Management (26%)
This domain revolves around the effective storage and cataloging of data. It encompasses various tasks, such as data modeling and schema definition for various data types, including structured, unstructured, or semi-structured data.
Candidates should possess comprehensive knowledge of AWS storage solutions and the capacity to select the most appropriate data store based on factors such as availability and throughput requirements. Additionally, managing data lifecycles in a cost-efficient, secure, and fault-tolerant manner is of paramount importance.
Domain 3: Understanding Data Operations and Support (22%)
In this domain, candidates are assessed on their ability to use AWS services for data analysis and maintain data quality through automated data processing. This involves configuring monitoring and logging for data pipelines and leveraging services like CloudTrail and CloudWatch to aid in troubleshooting operational issues.
Familiarity with AWS Glue DataBrew is also essential, as it plays a pivotal role in data preparation, transformation, defining data quality rules, and data verification and cleaning.
Domain 4: Understanding Data Security and Governance (18%)
The final domain places a strong emphasis on data security, authorization, and compliance. Candidates must comprehend the significance of security within an AWS architecture and the implementation of robust security measures within the VPC network infrastructure and for user access control via AWS Identity and Access Management (IAM).
This encompasses understanding the principle of least privilege and applying role-based, attribute-based, and policy-based security measures when applicable. Proficiency in encryption and the use of AWS Key Management Service (KMS) for data encryption and decryption is also indispensable.
These domains provide a comprehensive framework for assessing a candidate’s knowledge and skills in data engineering within the AWS environment, encompassing vital concepts and practices in data management, transformation, analysis, security, and governance.
AWS Certified Data Engineer – Associate Learning Resources
AWS Learning Resources
AWS offers a diverse range of learning resources to cater to individuals at various stages of their cloud computing journey. From beginners seeking foundational knowledge to experienced professionals aiming to refine their skills, AWS provides comprehensive documentation, tutorials, and hands-on labs. The AWS Training and Certification platform offers structured courses led by expert instructors, covering a wide array of topics from cloud fundamentals to specialized domains like machine learning and security. Some of them for AWS Data Engineer Associate exams are:
- Engage in the best of re:Invent Analytics 2022
- A Day in the Life of a Data Engineer
- Building Batch Data Analytics Solutions on AWS
Join Study Groups
Study groups offer a dynamic and collaborative approach to AWS exam preparation. By joining these groups, you gain access to a community of like-minded individuals who are also navigating the complexities of AWS certifications. Engaging in discussions, sharing experiences, and collectively tackling challenges can provide valuable insights and enhance your understanding of key concepts. Study groups create a supportive environment where members can clarify doubts, exchange tips, and stay motivated throughout their certification journey. This collaborative learning experience not only strengthens your grasp of AWS technologies but also fosters a sense of camaraderie among peers pursuing similar goals.
Use AWS Certified Data Engineer – Associate Practice Tests
Incorporating AWS practice tests into your preparation strategy is essential for achieving exam success. These practice tests simulate the actual exam environment, allowing you to assess your knowledge, identify areas for improvement, and familiarize yourself with the types of questions you may encounter. Regularly taking practice tests helps build confidence, refines your time-management skills, and ensures you are well-prepared for the specific challenges posed by AWS certification exams. The combination of study groups and practice tests creates a well-rounded and effective approach to mastering AWS technologies and earning your certification.
AWS Certified Data Engineer – Associate | Job Roles and Opportunities
Let us now have a look at different job opportunities which are available once you clear this certification.
Data Engineer / Big Data Engineer
A Data Engineer, often referred to as a Big Data Engineer in the context of managing and processing large datasets, is a specialized role within the field of data management and analytics. Data Engineers play a crucial role in the data pipeline by designing, building, and maintaining the infrastructure and systems necessary for collecting, storing, and processing data efficiently. Here’s a description of the role, along with salary information and growth opportunities:
Role Description:
- Data Ingestion: Data Engineers are responsible for developing systems to ingest data from various sources, including databases, APIs, logs, and external datasets.
- Data Storage: They design and maintain data storage solutions, including data warehouses, data lakes, and NoSQL databases, to ensure data is stored securely and is easily accessible for analysis.
- Data Transformation: Data Engineers perform data transformation and cleaning tasks to prepare the data for analysis, often using technologies like Apache Spark, Apache Hadoop, or ETL (Extract, Transform, Load) processes.
- Data Pipeline: They build and manage data pipelines to automate data workflows, ensuring a consistent flow of data from source to destination.
- Scalability: Data Engineers design systems that can scale horizontally to handle large volumes of data effectively.
- Data Governance: They implement data governance and security measures to protect sensitive data and ensure compliance with regulations.
- Collaboration: Data Engineers work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions that meet business needs.
Salary: The salary of a Data Engineer or Big Data Engineer can vary significantly depending on factors like location, experience, and the specific industry. Here is a rough estimate of salary ranges:
- Entry-Level: Entry-level Data Engineers can expect salaries ranging from $60,000 to $90,000 annually.
- Mid-Level: With a few years of experience, mid-level Data Engineers can earn salaries ranging from $90,000 to $130,000 or more annually.
- Experienced/Senior: Experienced Data Engineers, especially those with specialized skills or in-demand expertise, can command salaries exceeding $150,000 annually.
Keep in mind that these figures are approximate and can vary significantly based on factors like geographic location and the specific company’s compensation structure.
Growth Opportunities: The field of Data Engineering offers promising career growth opportunities:
- Specialization: Data Engineers can specialize in various areas such as streaming data, cloud-based data solutions (e.g., AWS, Azure, GCP), or specific industry domains (e.g., healthcare, finance).
- Management Roles: Experienced Data Engineers can move into leadership roles such as Data Engineering Manager or Chief Data Engineer, overseeing teams and strategic data initiatives.
- Data Architecture: Some Data Engineers transition into Data Architect roles, focusing on high-level data system design and strategy.
- Big Data Technologies: Staying updated with the latest big data technologies and tools can open up opportunities to work on cutting-edge projects.
- Data Science Transition: Some Data Engineers transition into data science roles after gaining a strong understanding of data and analytics.
- Consulting and Freelancing: Experienced Data Engineers may choose to work as independent consultants or freelancers, offering their expertise to multiple clients.
- Certifications and Education: Ongoing education and certifications in relevant technologies and methodologies can enhance career prospects.
The demand for skilled Data Engineers remains high, making it a rewarding and stable career path with opportunities for advancement and competitive compensation.
Senior Data Engineer
A Senior Data Engineer is a highly experienced and specialized professional within the field of data engineering. This role is typically responsible for designing, developing, and managing complex data infrastructure and systems to support data-driven applications and analytics. Here’s a detailed description of the role of a Senior Data Engineer:
Role Description:
- Data Architecture: Senior Data Engineers are responsible for designing and maintaining the overall data architecture of an organization. They define data storage solutions, data modeling approaches, and data integration strategies.
- Data Pipeline Development: They design, build, and optimize data pipelines to ensure the efficient and reliable flow of data from various sources to data warehouses or data lakes. This involves handling data transformation, cleansing, and enrichment processes.
- Big Data Technologies: Senior Data Engineers are well-versed in big data technologies such as Hadoop, Spark, and NoSQL databases. They leverage these technologies to process and analyze large volumes of data efficiently.
- Cloud Platforms: Many Senior Data Engineers work with cloud-based platforms such as AWS, Azure, or Google Cloud to build and manage data solutions. They are proficient in setting up cloud data services and optimizing their performance.
- Data Governance: Ensuring data quality, security, and compliance is a key responsibility. They implement data governance policies and security measures to protect sensitive data.
- Team Leadership: In some cases, Senior Data Engineers may lead teams of data engineers and collaborate with data scientists, analysts, and other stakeholders to deliver data solutions.
- Performance Optimization: They focus on optimizing data systems for performance, scalability, and cost-efficiency. This includes tuning queries, selecting appropriate data storage solutions, and monitoring system performance.
- Problem Solving: Senior Data Engineers are skilled problem solvers, capable of identifying and resolving data-related issues and bottlenecks in data pipelines.
Skills and Qualifications:
- Extensive experience in data engineering, typically 5+ years.
- Proficiency in programming languages such as Python, Java, or Scala.
- Strong knowledge of data storage and processing technologies, including relational databases, data warehouses, and big data frameworks.
- Expertise in ETL (Extract, Transform, Load) processes and data integration.
- Familiarity with data modeling and database design principles.
- Cloud platform certification (e.g., AWS Certified Data Analytics, Azure Data Engineer) is often preferred.
- Excellent problem-solving and analytical skills.
- Strong communication skills for collaborating with cross-functional teams.
Salary:
Salaries for Senior Data Engineers can vary widely depending on factors like location, industry, and level of experience. On average, Senior Data Engineers can expect to earn salaries ranging from $120,000 to $180,000 or more annually, with the potential for even higher earnings in areas with a high demand for data engineering expertise.
Growth Opportunities:
Senior Data Engineers often have the opportunity to advance into roles such as Data Engineering Manager, Principal Data Engineer, or Chief Data Engineer. They can also choose to specialize further in areas like data architecture, machine learning engineering, or cloud architecture. Continuing education and certifications can further enhance career prospects in this dynamic field.
Cloud Data Engineer
A Cloud Data Engineer is a specialized professional responsible for designing, building, and managing data infrastructure and solutions in cloud computing environments. This role is critical for organizations that rely on cloud platforms to store, process, and analyze data. Here’s a comprehensive description of the role of a Cloud Data Engineer:
Role Description:
- Data Infrastructure Design: Cloud Data Engineers are responsible for designing data architectures and infrastructure on cloud platforms like AWS, Azure, Google Cloud, or others. They determine the best cloud services and components for storing and processing data efficiently.
- Data Integration: They develop and maintain data pipelines, ensuring data from various sources is collected, transformed, and loaded into data warehouses, data lakes, or other storage solutions in the cloud.
- Big Data Technologies: Proficiency in big data technologies like Apache Spark, Hadoop, and data streaming platforms is essential. They use these tools to process and analyze large datasets effectively.
- Cloud Services: Cloud Data Engineers work with a wide range of cloud services, including databases (e.g., AWS RDS, Azure SQL Database), data warehouses (e.g., AWS Redshift, Google BigQuery), and storage solutions (e.g., AWS S3, Azure Data Lake Storage).
- Data Security and Compliance: Ensuring data security and compliance with relevant regulations is a priority. They implement access controls, encryption, and auditing mechanisms to protect sensitive data.
- Data Governance: Implementing data governance policies and best practices to maintain data quality, accuracy, and consistency.
- Scalability: Designing systems that can scale horizontally to handle increased data volumes and processing requirements as the organization grows.
- Performance Optimization: Tuning and optimizing data pipelines, queries, and database performance for cost-efficiency and speed.
- Monitoring and Troubleshooting: Implementing monitoring and logging solutions to track system health and troubleshoot issues in real-time.
Skills and Qualifications:
- Proficiency in cloud platforms such as AWS, Azure, or Google Cloud.
- Strong programming skills in languages like Python, Java, or Scala.
- Knowledge of big data technologies and frameworks.
- Experience with ETL (Extract, Transform, Load) processes.
- Familiarity with data modeling, database design, and SQL.
- Understanding of data security and compliance best practices.
- Cloud certifications, such as AWS Certified Data Analytics or Azure Data Engineer, are often preferred.
- Problem-solving and analytical skills.
- Strong communication skills for collaboration with cross-functional teams.
Salary:
Salaries for Cloud Data Engineers vary based on factors like experience, location, and industry. On average, Cloud Data Engineers can expect to earn salaries ranging from $90,000 to $150,000 or more annually, with the potential for higher earnings in areas with a strong demand for cloud expertise.
Growth Opportunities:
Cloud Data Engineers have various growth opportunities within their career path, including:
- Senior Cloud Data Engineer: With experience, Cloud Data Engineers can advance to senior roles with more responsibilities and higher salaries.
- Data Architect: Some professionals choose to specialize further in data architecture, focusing on high-level design and strategy.
- Machine Learning Engineer: Transitioning into roles related to machine learning and AI is also an option, given the overlap in skills and tools.
- Data Engineering Manager: Moving into management positions to lead teams of data engineers and oversee data projects.
- Cloud Solutions Architect: Specializing in cloud architecture and helping organizations design overall cloud strategies.
- Consulting and Freelancing: Experienced Cloud Data Engineers may work as independent consultants or freelancers, offering their expertise to multiple clients.
Continuing education and staying up-to-date with the latest cloud technologies and trends can open up new career opportunities in this dynamic field.
Data Architect
A Data Architect is a professional responsible for designing, organizing, and managing an organization’s data infrastructure and systems. They play a pivotal role in ensuring that data is stored, processed, and used effectively to meet business objectives. Here’s a detailed description of the role of a Data Architect:
Role Description:
- Data Strategy: Data Architects develop and implement data strategies that align with an organization’s overall business goals and objectives. They define the vision for data management and guide data-related decisions.
- Data Modeling: They design data models that define the structure and relationships of data elements. This includes creating conceptual, logical, and physical data models to ensure data accuracy and consistency.
- Database Design: Data Architects are responsible for selecting and designing database systems, whether relational databases, NoSQL databases, data warehouses, or data lakes, to meet specific data storage and processing requirements.
- Data Integration: They oversee data integration processes, ensuring that data flows seamlessly between systems and applications. This involves designing and managing ETL (Extract, Transform, Load) pipelines.
- Data Governance: Implementing data governance policies and practices to maintain data quality, security, and compliance with relevant regulations. This includes defining data standards, access controls, and data retention policies.
- Performance Optimization: Tuning and optimizing database performance to ensure efficient data retrieval and processing. This includes indexing, query optimization, and scaling solutions.
- Data Security: Ensuring data security by implementing encryption, access controls, and auditing mechanisms to protect sensitive data from unauthorized access or breaches.
- Cloud Integration: Many Data Architects work with cloud platforms, designing data solutions that leverage the capabilities of cloud services like AWS, Azure, or Google Cloud.
- Data Documentation: Maintaining comprehensive documentation of data models, schemas, and data flow diagrams to aid in data understanding and collaboration.
Skills and Qualifications:
- Extensive experience in database design, data modeling, and data management.
- Proficiency in database technologies such as SQL, NoSQL, and data warehousing.
- Strong knowledge of data governance, data security, and compliance best practices.
- Familiarity with ETL processes and data integration tools.
- Understanding of cloud platforms and services.
- Excellent problem-solving and analytical skills.
- Effective communication and collaboration skills to work with cross-functional teams.
Salary: Salaries for Data Architects can vary widely depending on factors like experience, location, and industry. On average, Data Architects can expect to earn salaries ranging from $100,000 to $160,000 or more annually, with the potential for higher earnings in areas with high demand for data expertise.
Growth Opportunities: Data Architects have various growth opportunities within their career path, including:
- Senior Data Architect: With experience, Data Architects can advance to senior roles with more responsibilities and higher salaries.
- Enterprise Architect: Transitioning into broader enterprise architecture roles, where they focus on aligning technology solutions with overall business strategies.
- Chief Data Officer (CDO): In some organizations, Data Architects may aspire to become CDOs, leading the overall data strategy and governance.
- Consulting: Some Data Architects choose to work as independent consultants, offering their expertise to multiple clients.
- Data Engineering Manager: Moving into management positions to lead teams of data engineers and oversee data projects.
Continuing education, staying updated with emerging technologies, and obtaining relevant certifications (e.g., Certified Data Management Professional, AWS Certified Data Analytics) can enhance career prospects in this field.
Business Intelligence Engineer
A Business Intelligence (BI) Engineer is a professional responsible for designing, developing, and maintaining the technology infrastructure and tools necessary to support data analysis and reporting in an organization. They play a critical role in transforming raw data into meaningful insights that inform business decisions. Here’s a detailed description of the role of a Business Intelligence Engineer:
Role Description:
- Data Gathering: BI Engineers collect and integrate data from various sources, including databases, data warehouses, cloud platforms, and external data feeds.
- Data Transformation: They cleanse, transform, and prepare data for analysis, ensuring it is accurate and consistent. This often involves using ETL (Extract, Transform, Load) processes and tools.
- Data Modeling: BI Engineers design data models and schemas that facilitate efficient querying and reporting. They create logical and physical data models to structure the data for analysis.
- Reporting and Dashboard Development: They develop reports, dashboards, and visualizations using BI tools like Tableau, Power BI, or QlikView. These tools allow end-users to interact with data and gain insights.
- Data Warehousing: BI Engineers may be responsible for designing and maintaining data warehousing solutions, which serve as centralized repositories for historical data used in reporting and analysis.
- Performance Optimization: They optimize queries, database structures, and data processing workflows to ensure that data is retrieved and analyzed quickly and efficiently.
- Data Security and Compliance: Ensuring data security and compliance with relevant regulations, including access controls and data protection measures, is a crucial aspect of the role.
- Collaboration: BI Engineers collaborate with business analysts, data scientists, and other stakeholders to understand data requirements and deliver relevant solutions.
- Documentation: Maintaining documentation of data models, data sources, and reporting processes to ensure that knowledge is shared and available to the team.
Skills and Qualifications:
- Proficiency in SQL for querying and manipulating data.
- Experience with data visualization tools like Tableau, Power BI, or similar.
- Knowledge of ETL processes and data integration.
- Strong problem-solving and analytical skills.
- Familiarity with data warehousing concepts and solutions.
- Understanding of data security and compliance.
- Effective communication and collaboration skills.
Salary:
Salaries for BI Engineers can vary depending on factors like experience, location, and the specific industry. On average, BI Engineers can expect to earn salaries ranging from $80,000 to $130,000 or more annually, with potential variations based on the organization’s size and complexity.
Growth Opportunities:
BI Engineers have various growth opportunities within their career path, including:
- Senior BI Engineer: With experience, BI Engineers can advance to senior roles with more responsibilities and higher salaries.
- BI Manager: Transitioning into management positions to lead teams of BI professionals and oversee BI projects.
- Data Analyst or Data Scientist: Transitioning into roles that involve more advanced data analysis or machine learning tasks.
- Data Architect: Specializing in data architecture and designing high-level data solutions.
- Consulting: Some BI Engineers choose to work as independent consultants, offering their expertise to multiple clients.
- Data Engineering: Transitioning into roles in data engineering, which involve designing and managing data pipelines and infrastructure.
- Certifications: Obtaining relevant certifications in BI tools and technologies can enhance career prospects. For example, Tableau and Power BI offer certification programs.
Continuing education, staying updated with BI trends and technologies, and obtaining certifications can help BI Engineers progress in their careers and take on more challenging roles in the field.
Expert Corner
The AWS Certified Data Engineer – Associate (DEA-C01) Exam serves as an entry point for individuals who lack a prior background in data but are eager to get into more advanced specialty subjects. On the other hand, for individuals already working in data-related positions, this certification presents an exceptional opportunity to broaden their AWS expertise by leveraging specialized services with which they may already be qualified.
While gaining these skills has always been feasible without formal certification, the introduction of a structured certification pathway serves as motivation for learners to actively seek certification. The blog provides a list of resources and guidelines that will help you smoothen your learning journey for better experience.
