The world of cloud computing continues to evolve at a rapid pace, with organizations relying increasingly on sophisticated data processing and analytics platforms to drive business decisions. Among the most sought-after skills in this domain is the ability to manage, process, and analyze large volumes of data on cloud platforms, specifically Google Cloud Platform (GCP). If you are looking to carve out a lucrative career as a GCP Data Engineer, acquiring the Google Cloud Professional Data Engineer certification is a powerful way to validate your skills and open doors to higher salary opportunities and exciting job roles.
In this series, we will dive deep into the core aspects of the GCP Data Engineer role, the benefits of certification, and what it takes to successfully pass the Google Cloud Professional Data Engineer exam. Understanding these fundamentals will help you establish a clear roadmap and get ready to take your career to the next level.
Why Choose a Career as a GCP Data Engineer?
Data engineers play a critical role in organizations by building and managing the infrastructure that supports data generation, processing, storage, and analysis. With the explosion of big data and cloud technologies, Google Cloud Platform has become one of the premier ecosystems for managing data workflows at scale.
The demand for skilled GCP Data Engineers is soaring, driven by organizations seeking experts who can design and operationalize data pipelines, manage databases, and enable data-driven decision-making. Professionals with the Google Cloud Data Engineer certification often command higher salaries, reflecting their advanced knowledge and proven expertise.
Beyond salary, this certification empowers you with practical skills that are immediately applicable in real-world environments, including the design of secure, reliable, and flexible data processing systems on GCP.
Understanding the Role of a GCP Data Engineer
The GCP Data Engineer certification exam tests your ability to handle various responsibilities, including designing data processing systems, ingesting and transforming data, storing and analyzing datasets, automating workflows, and ensuring the security and compliance of data solutions.
Typical tasks for a GCP Data Engineer include:
- Designing secure and compliant data architectures using GCP tools like Cloud IAM, Data Loss Prevention API, and encryption technologies.
- Building and monitoring data pipelines using services such as Dataflow, Dataproc, and Pub/Sub.
- Managing data storage solutions, including BigQuery, Cloud Storage, Cloud SQL, and Bigtable, based on access patterns and cost-performance considerations.
- Preparing data for analysis and visualization through tools like BigQuery materialized views and Analytics Hub.
- Automating and optimizing data workloads with Cloud Composer, Workflows, and CI/CD pipelines.
- Troubleshooting data processes, ensuring fault tolerance, and managing disaster recovery.
GCP Data Engineers not only focus on technology but also need to align data solutions with business needs, ensuring data governance, portability, and scalability.
Prerequisites and Experience Requirements
Although the Google Cloud Professional Data Engineer certification does not require formal prerequisites, it is highly recommended that candidates possess a minimum of three years of industry experience working with data and at least one year of hands-on experience designing and managing data solutions on GCP.
Experience in cloud architecture, data modeling, data warehousing, and pipeline orchestration will greatly enhance your readiness for the exam and subsequent job roles. Familiarity with programming languages and tools commonly used in data engineering, such as SQL, Python, Apache Beam, and Apache Spark, is also beneficial.
Candidates must be at least 18 years old to appear for the certification exam.
Key Details About the GCP Data Engineer Exam
To plan your preparation effectively, understanding the exam structure and logistics is crucial.
The exam consists of multiple-choice and multiple-select questions designed to evaluate your practical and theoretical knowledge of data engineering on Google Cloud. Candidates have two hours to complete the exam, which is available in English, Portuguese, Japanese, and Spanish.
The exam registration fee is USD 200, plus applicable taxes. You can schedule your exam at authorized test centers or take it online, depending on availability and preference.
Exam Objectives and Domains
The certification exam evaluates five major domains, each focusing on different aspects of data engineering:
- Designing data processing systems (22%)
- Ingesting and processing data (25%)
- Storing data (20%)
- Preparing and using data for analysis (15%)
- Maintaining and automating data workloads (18%)
Each domain covers specific skills and concepts that you must master to pass the exam and excel as a GCP Data Engineer.
Designing Data Processing Systems
In this domain, you will be tested on your ability to architect systems that meet security, compliance, reliability, flexibility, and migration requirements. You should be familiar with concepts like Identity and Access Management, data encryption, privacy tools, regional data sovereignty, and compliance frameworks.
Additionally, this section assesses your skills in preparing and cleaning data, orchestrating pipelines, ensuring fault tolerance and disaster recovery, and mapping business needs to scalable and portable architectures.
Ingesting and Processing Data
The second domain focuses on building data pipelines that can ingest data from diverse sources and process it efficiently. You need to understand data sources and sinks, batch and streaming data processing, services like Dataflow and Dataproc, and strategies for automating job deployment and orchestration using Cloud Composer and Workflows.
Storing Data
Choosing appropriate storage solutions based on workload requirements, data access patterns, and cost considerations is key here. You will need to demonstrate expertise in selecting managed storage services such as Bigtable, Cloud Spanner, Cloud SQL, and Cloud Storage.
Understanding data warehouse design with BigQuery, data lakes, and newer concepts like data mesh architecture will also be tested.
Preparing and Using Data for Analysis
This domain covers preparing datasets for visualization and analysis, sharing and publishing data securely, and enabling data discovery and exploration.
You will be expected to know how to optimize query performance, use materialized views, and implement access controls. Familiarity with analytics tools and the ability to prepare data for machine learning workflows will be important.
Maintaining and Automating Data Workloads
Finally, the exam tests your ability to maintain efficient data pipelines by optimizing resource usage, designing repeatable workflows, scheduling jobs, monitoring data processes, and troubleshooting issues. This includes implementing fault-tolerant designs and managing high availability through replication and failover strategies.
Mastering the GCP Data Engineer Certification Exam: Preparation Strategies and Course Breakdown
In the previous article, we explored the essential role of a Google Cloud Platform Data Engineer, the exam domains, and the benefits of earning the certification. Now, let’s shift our focus to effective preparation strategies and a detailed look at the key topics covered in the GCP Data Engineer certification exam. A thorough understanding of these topics and a structured study approach will increase your chances of passing the exam with confidence.
Planning Your Certification Journey
Preparation for the GCP Data Engineer certification exam requires both theoretical knowledge and practical skills. Since the exam tests real-world scenarios involving designing and operationalizing data pipelines and architectures on Google Cloud, hands-on experience is invaluable.
Start by setting a clear study schedule. Aim to dedicate consistent daily or weekly time slots toward studying, balancing video lessons, reading official documentation, and practicing with sample questions. Because the exam covers a broad range of subjects, breaking down the content into manageable modules helps maintain focus and retention.
Successfully earning the GCP Data Engineer certification requires more than just understanding the technical concepts; it demands a well-thought-out plan that aligns your study habits, resources, and goals. Proper planning is the cornerstone of effective preparation and can dramatically increase your chances of passing the exam on your first attempt.
The first step in planning your certification journey is to assess your current knowledge and experience. The GCP Data Engineer exam covers a broad range of topics—from designing data processing systems to managing data workflows and optimizing performance. Reflect honestly on your strengths and areas where you might need improvement. This self-assessment will help you focus your efforts on the most critical and challenging topics, rather than trying to cover everything superficially.
Setting clear, realistic goals is equally important. Break down your preparation into manageable milestones. For example, you might plan to complete certain course modules each week, allocate time for hands-on labs, and schedule regular practice exams. Creating a study calendar with specific deadlines will keep you accountable and motivated, reducing the risk of procrastination.
Choosing the right learning materials is another key aspect of your plan. Comprehensive online courses tailored to the GCP Data Engineer exam can provide structured guidance through the exam domains. These courses often include video lectures, reading materials, and quizzes to reinforce your understanding. Complement these with Google’s official documentation, which is invaluable for deep dives into specific technologies and features.
Hands-on practice cannot be overstated. The exam tests your practical skills in designing, building, and managing data systems on Google Cloud. Setting up your own Google Cloud environment to experiment with services like BigQuery, Dataflow, Pub/Sub, and Cloud Storage will help solidify your knowledge. Many platforms offer labs and sandbox environments that simulate real-world scenarios, giving you a safe space to apply what you’ve learned without the risk of incurring charges or disrupting production systems.
As part of your planning, integrate regular review sessions. Revisiting previously studied material ensures that knowledge is retained over time and not forgotten. Using flashcards, summary notes, or mind maps can aid in consolidating information. Also, try to explain complex concepts in your own words or teach them to a peer; this active learning approach boosts comprehension and retention.
Practice exams and mock tests are vital tools in your certification journey. They familiarize you with the exam format, question styles, and time constraints. After each practice test, spend time analyzing your mistakes and understanding why certain answers were incorrect. This reflective process helps identify weak spots and prevents repeating the same errors.
Don’t neglect the importance of taking care of yourself during this intense preparation period. Ensure you get enough rest, maintain a balanced diet, and take breaks to avoid burnout. Mental and physical well-being directly impact your ability to focus and absorb new information.
Joining study groups or online forums can add a social dimension to your learning. Engaging with fellow candidates allows you to exchange tips, clarify doubts, and gain different perspectives on challenging topics. These communities often share valuable insights and resources that can enrich your study plan.
Finally, plan for the logistics of the exam itself. Familiarize yourself with the registration process, exam policies, and available languages. Schedule your exam date with enough buffer to prepare thoroughly, but also to maintain momentum. Knowing where the test center is or how to set up your remote proctoring environment will reduce stress on exam day.
By thoughtfully organizing your certification journey, you transform what might seem like an overwhelming task into a clear, achievable path. This strategic approach not only prepares you for the exam but also equips you with lifelong learning skills that will benefit your professional development beyond certification.
Remember, the journey to becoming a certified GCP Data Engineer is as important as the destination. Planning it well ensures you stay on track, maintain confidence, and ultimately achieve your certification goals with success.
Recommended Study Resources
To master the concepts tested in the exam, use a combination of learning materials:
- Video Training Modules: Structured video courses covering each domain and subtopic are crucial for building a strong foundation.
- Official Google Cloud Documentation: Google provides comprehensive and up-to-date documentation on all services, from BigQuery and Dataflow to Cloud IAM and data governance.
- Practice Exams: Mock tests simulating the actual exam format help familiarize you with question styles and time management.
- Hands-on Labs: Interactive labs and sandbox environments allow you to experiment with GCP tools and workflows.
Leveraging these resources ensures a well-rounded preparation that addresses both conceptual understanding and practical application.
In-Depth Course Topics for the GCP Data Engineer Exam
The certification exam covers five major domains, each requiring a focused study approach. Below, we break down each domain with key topics you must master.
1. Designing Data Processing Systems
Designing robust data architectures is foundational. Key areas include:
- Security and Compliance: Implementing Identity and Access Management (IAM) policies, encryption at rest and in transit, and data privacy with Cloud Data Loss Prevention API.
- Reliability and Fault Tolerance: Building pipelines that can handle failures gracefully, incorporating disaster recovery plans, and ensuring ACID compliance where necessary.
- Flexibility and Portability: Designing systems that accommodate evolving business needs, support multi-cloud strategies, and enable data governance through cataloging and discovery.
- Data Migrations: Planning and executing migrations to GCP using services like Database Migration Service and Transfer Appliance while validating data integrity.
2. Ingesting and Processing Data
Efficient ingestion and processing ensure data flows smoothly from sources to analysis platforms:
- Data Pipeline Planning: Identifying sources and sinks, defining transformation logic, and securing data with encryption.
- Building Pipelines: Using tools such as Dataflow, Apache Beam, Dataproc, and Pub/Sub to process batch and streaming data, perform data cleansing, and handle late-arriving data.
- Operationalizing Pipelines: Automating workflows with Cloud Composer, managing CI/CD pipelines, and ensuring reliable job execution.
3. Storing Data
Choosing the right storage strategy optimizes performance and cost:
- Storage Systems: Evaluating managed services, including Bigtable, Cloud SQL, Cloud Spanner, and Cloud Storage based on data access patterns.
- Data Warehouse Design: Creating efficient data models in BigQuery, understanding normalization, and mapping business requirements.
- Data Lakes and Data Mesh: Managing data lakes with proper security, cost controls, and processing pipelines. Implementing distributed data mesh architectures for federated governance.
4. Preparing and Using Data for Analysis
Data engineers enable analytics teams by preparing data for visualization and exploration:
- Data Preparation: Creating materialized views, optimizing query performance, and troubleshooting slow queries.
- Data Sharing: Establishing secure sharing protocols, publishing datasets and reports, and utilizing Analytics Hub.
- Exploration and Feature Engineering: Preparing datasets for machine learning workflows and conducting data discovery.
5. Maintaining and Automating Data Workloads
Efficient operation of data pipelines reduces downtime and cost:
- Resource Optimization: Managing cost-effective resource allocation, choosing between persistent clusters and job-based resources.
- Automation: Designing repeatable workflows with Airflow DAGs, scheduling jobs, and orchestrating complex data processes.
- Monitoring and Troubleshooting: Using Cloud Monitoring and Logging to observe pipeline health, handle quota issues, and manage workload capacity.
- Fault Tolerance and Failover: Implementing multi-region job execution, handling data corruption, and designing systems for high availability.
Practice and Review: The Final Steps to Certification
Once you’ve covered the material, practice is the key to success. Regularly test yourself with scenario-based questions and timed practice exams. Focus on areas where you struggle and revisit documentation or video lessons as needed.
Additionally, participate in community forums or study groups. Engaging with peers allows you to discuss challenging concepts, share tips, and gain insights from diverse experiences.
Real-World Applications and Advanced Skills for GCP Data Engineers
In the last article, we discussed effective preparation strategies and a detailed breakdown of the exam domains for the GCP Data Engineer certification. Now, it’s time to focus on real-world applications of these skills and the advanced competencies that set exceptional data engineers apart. Understanding how to apply theoretical knowledge practically will empower you to design, build, and optimize data systems in complex environments.
Applying GCP Data Engineering Skills in the Real World
A certified GCP Data Engineer’s role extends beyond passing the exam — it involves building scalable, reliable, and secure data solutions that meet business needs. Let’s look at key practical applications:
Designing Scalable Data Pipelines
Data engineers often work with enormous volumes of data streaming in from diverse sources. Building scalable pipelines that handle batch and real-time data ingestion is critical. Leveraging tools like Cloud Dataflow and Apache Beam allows the processing of both streaming and batch data uniformly. Real-world scenarios include:
- Processing real-time sensor data in IoT applications.
- Ingesting user behavior data from websites and apps for analytics.
- Managing large batch ETL workflows from on-premises databases to BigQuery.
Efficient pipeline design involves optimizing resource use, handling late-arriving or duplicate data, and ensuring fault tolerance to minimize downtime.
Data Storage and Management
Choosing the right storage service depends on your use case:
- Use BigQuery for large-scale data warehousing and analytics with SQL-like querying capabilities.
- Choose Cloud Bigtable for low-latency NoSQL access suited to time-series or key-value workloads.
- Utilize Cloud Spanner for globally distributed relational databases with strong consistency.
Proper data lifecycle management and cost optimization strategies, such as data partitioning and tiered storage, ensure the solution remains cost-effective as data volumes grow.
Data Security and Compliance
With increasing regulations around data privacy, implementing security best practices is non-negotiable:
- Use Cloud IAM roles to enforce the principle of least privilege.
- Encrypt data at rest and in transit.
- Employ Cloud Data Loss Prevention (DLP) APIs to identify and mask sensitive information.
- Plan for compliance with regional data sovereignty laws by controlling data location.
Security considerations must be embedded at every stage, from ingestion to storage to analysis.
Advanced Skills for GCP Data Engineers
To stand out as a GCP Data Engineer, beyond mastering exam topics, cultivating advanced skills is essential.
Automation and Orchestration
Manual data pipeline management is not scalable. Automation tools such as Cloud Composer (built on Apache Airflow) enable scheduling, monitoring, and managing complex workflows with dependencies. Developing proficiency in writing Directed Acyclic Graphs (DAGs) and integrating them with CI/CD pipelines improves pipeline reliability and maintainability.
Machine Learning Integration
Data engineers often collaborate with data scientists and ML engineers. Familiarity with TensorFlow and GCP’s AI tools helps prepare data for machine learning:
- Implement feature engineering pipelines.
- Use BigQuery ML for building and deploying ML models directly within the data warehouse.
- Manage model training and deployment workflows efficiently.
Understanding these integrations accelerates time-to-insight and adds value to business processes.
Monitoring, Troubleshooting, and Optimization
Proactive monitoring using Cloud Monitoring and Cloud Logging is vital for maintaining pipeline health. Advanced data engineers set up alerts for SLA breaches, automate troubleshooting steps, and optimize query performance by analyzing execution plans.
Optimizing resource allocation, balancing cost and performance, and designing systems for fault tolerance reduce downtime and operational expenses.
Real-World Challenges and Best Practices
Even experienced data engineers face common challenges:
- Handling schema changes in streaming data.
- Balancing data freshness and processing cost.
- Ensuring data quality and consistency across distributed systems.
Adopting best practices like modular pipeline design, continuous testing, and version control can mitigate these issues.
Maximizing Your Career Potential with GCP Data Engineer Certification
After understanding the exam structure, mastering essential concepts, and applying skills to real-world scenarios, the final step in your journey is to leverage your GCP Data Engineer certification to advance your career. This article will guide you through strategic career planning, effective exam preparation techniques, and the tangible benefits that certification offers in the competitive job market.
How the GCP Data Engineer Certification Advances Your Career
The technology landscape today is data-driven, and organizations heavily rely on data engineers to build the backbone of their analytics and machine learning initiatives. Achieving a GCP Data Engineer certification validates your expertise with one of the fastest-growing cloud platforms globally, opening numerous professional doors.
Increased Marketability and Salary Potential
Certified data engineers are in high demand. Companies seeking to leverage Google Cloud Platform technologies look for individuals who can demonstrate hands-on skills validated by certification. This credential signals to employers that you possess:
- Practical knowledge of Google Cloud data tools and services.
- The ability to design secure, scalable, and reliable data systems.
- Proficiency in data pipeline automation and optimization.
This credibility often translates into better job offers, higher salaries, and increased bargaining power during negotiations.
Career Opportunities and Roles
Certification can propel you into roles such as:
- Cloud Data Engineer
- Data Architect
- Big Data Engineer
- Machine Learning Engineer (with additional ML skills)
- Analytics Engineer
These roles vary in responsibilities but consistently demand strong data engineering fundamentals and Google Cloud proficiency.
Pathway to Specialization and Growth
The certification serves as a foundation to explore advanced cloud specializations or managerial tracks. For example, you might deepen expertise in AI and machine learning, DevOps, or cloud security. Alternatively, certification experience can help you transition into leadership roles overseeing data teams and projects.
Exam Preparation Best Practices for Success
Passing the GCP Data Engineer certification exam requires more than just understanding concepts; it demands a structured and strategic approach to studying and practice.
Understand the Exam Objectives Thoroughly
Start by reviewing the official exam guide detailing the domains covered and their weightages. Focus your study on these key areas:
- Designing data processing systems
- Building and operationalizing data pipelines
- Managing storage solutions
- Preparing data for analysis
- Automating and maintaining data workflows
Using this roadmap keeps your preparation aligned with what the exam expects.
Leverage Comprehensive Study Resources
Utilize a mix of learning materials, such as:
- Video tutorials and lectures for visual and practical understanding.
- Documentation and whitepapers from Google Cloud for official insights.
- Hands-on labs and projects to apply concepts in realistic environments.
Hands-on practice with GCP services is particularly critical because the exam tests your ability to solve problems using the platform.
Practice with Sample Questions and Mock Exams
Familiarize yourself with the exam format—multiple-choice and multiple-select questions—by practicing with mock tests. This builds exam-taking stamina and improves time management.
Analyzing incorrect answers helps identify knowledge gaps, enabling targeted review.
Create a Study Schedule and Stick to It
Consistency beats cramming. Dedicate specific hours daily or weekly to your preparation. Break down the syllabus into manageable modules and set milestones.
Regular revision of topics covered ensures retention over time.
Join Study Groups and Forums
Engaging with peers preparing for the same certification provides motivation, answers to doubts, and exposure to different perspectives. Platforms like online forums and social media groups can be valuable resources.
Exam Day Strategies and Tips
On exam day, focus on maximizing your performance with these tips:
- Get a good night’s sleep before the exam.
- Arrive early or log in ahead if taking the exam online.
- Read questions carefully, especially for multiple-choice items.
- Eliminate wrong answers to improve your chances on tough questions.
- Manage your time to avoid rushing through the last questions.
- Stay calm and confident; stress can impair judgment.
Beyond Certification: Continuing Professional Development
Certification is not the end but the beginning of your professional journey. The tech field evolves rapidly, and continuous learning keeps you relevant.
Keep Up-to-Date with Google Cloud Innovations
Google frequently updates its services and introduces new ones. Subscribe to official blogs, attend webinars, and participate in community events to stay current.
Apply Skills on Real Projects
Look for opportunities to implement what you’ve learned at work or through freelancing. Real-world experience reinforces your knowledge and demonstrates your capabilities to employers.
Pursue Advanced Certifications and Specializations
Consider additional certifications such as:
- Google Cloud Professional Cloud Architect
- Google Cloud Professional Machine Learning Engineer
- Certifications in specific GCP tools like Kubernetes or Security
These broaden your expertise and career options.
Contribute to the Community
Sharing knowledge through blogs, presentations, or open-source projects not only helps others but also deepens your understanding and raises your professional profile.
Earning your GCP Data Engineer certification is a significant achievement that validates your expertise in one of today’s most sought-after technology areas. It offers a competitive edge in the job market, paves the way for diverse career opportunities, and equips you with skills to tackle complex data challenges.
Success in this journey requires commitment, strategic preparation, and continuous learning. By leveraging practical experience, staying updated with evolving technologies, and engaging with the professional community, you can maximize the value of your certification and build a rewarding career as a GCP Data Engineer.
Now is the time to put your plans into action. Prepare diligently, master the exam, and step confidently into the future of cloud data engineering.
Final Thoughts
Becoming a certified GCP Data Engineer is more than just passing an exam; it is a transformative step toward positioning yourself at the forefront of cloud data technology. The journey you undertake prepares you not only to clear certification but to thrive in a field that is shaping the future of how organizations manage and leverage data.
One of the most important things to remember is that the certification validates both your knowledge and your practical skills. Google Cloud Platform offers a powerful and diverse ecosystem of data services, and mastering these tools enables you to design scalable, secure, and efficient data solutions that drive business success. This hands-on expertise is what truly sets you apart from others in the industry.
Throughout the preparation process, you will encounter challenges—complex concepts, unfamiliar technologies, and sometimes overwhelming information. Embrace these hurdles as growth opportunities. Each problem solved, each concept mastered, strengthens your ability to handle real-world scenarios with confidence and creativity. Don’t rush through the material. Instead, focus on deep understanding and practical application. This mindset will ensure that your skills remain relevant even as technologies evolve.
Furthermore, the value of this certification extends beyond individual achievement. Certified GCP Data Engineers contribute directly to their organizations’ strategic goals by enabling data-driven decisions, optimizing workflows, and implementing automation that enhances productivity. Your expertise will empower teams, foster innovation, and improve outcomes—qualities that make you an indispensable asset in any data-centric company.
Networking and community engagement are also critical components of your professional growth. By connecting with other cloud professionals, attending industry events, and participating in forums or user groups, you stay informed about emerging trends and best practices. Sharing your knowledge through writing, mentoring, or speaking at meetups not only reinforces your understanding but also builds your reputation as a thought leader.
Remember, certification is not a one-time destination but a continuous journey. Technology never stands still, and neither should your learning. Google Cloud regularly updates its services and introduces new capabilities, reflecting the fast-paced nature of the cloud industry. Staying proactive about upskilling ensures you remain competitive and ready to seize new opportunities.
Moreover, consider complementing your GCP Data Engineer certification with other specialized credentials or skills, such as machine learning, cloud architecture, or security. These additional competencies will broaden your career pathways and enable you to take on more complex, multidisciplinary roles.
From a career perspective, this certification opens doors to exciting roles across industries, from tech startups to large enterprises, financial institutions, healthcare, and government agencies. As businesses increasingly migrate to the cloud and generate vast amounts of data, the demand for professionals who can architect and maintain cloud-native data pipelines will only grow.
In addition, the GCP Data Engineer certification can enhance your professional confidence. Knowing that you have met rigorous standards and demonstrated your capabilities provides a sense of accomplishment and motivation to continue advancing. It also equips you to better communicate your skills to recruiters, hiring managers, and peers.
Finally, keep in mind that the journey you are on reflects a commitment to excellence and adaptability—traits that are highly valued in today’s dynamic job market. The discipline you cultivate during your preparation will serve you well in other areas of your career and life.
In summary, earning the GCP Data Engineer certification is a powerful career investment. It validates your expertise, expands your professional horizons, and prepares you to contribute meaningfully in a data-driven world. Embrace the learning process, stay curious, and remain dedicated. With persistence and passion, you will not only pass the exam but also build a rewarding, future-proof career as a Google Cloud Data Engineer.