BigQuery Platform Architect / DBA

A BigQuery Platform Architect/DBA at Wavicle is responsible for setting standards for BigQuery environments, ensuring performance and scalability. This role includes designing, implementing, and maintaining applications that utilize BigQuery, leading a team of engineers to manage the overall environment, and implementing security controls. Additionally, the position requires collaboration with data teams while managing data integration tasks from various sources to BigQuery.

To qualify for the BigQuery Platform Architect/DBA position at Wavicle, candidates should possess a minimum of three years of experience with Google BigQuery and five years with Google Cloud Platform. Proficiency in SQL, database performance tuning, scripting languages, and data governance is essential. Familiarity with ETL tools and experience in cloud migrations is a plus, alongside strong communication skills for effective teamwork.

At Wavicle, a BigQuery Platform Architect/DBA can expect significant career growth. The role offers opportunities to expand skills in cloud technologies, lead teams on innovative projects, and contribute to strategic decisions. With a culture that encourages continuous learning and professional development, there are pathways to advance into managerial roles or specialized positions in cloud architecture or data science.

Wavicle is committed to fostering a diverse and inclusive workplace, exemplified by our workforce which has over 40% women. To support this, we promote a culture where all voices are heard, and individuals are encouraged to bring their unique perspectives. Our collaborative environment allows for innovative problem-solving, ensuring that all employees, including BigQuery Platform Architects/DBAs, feel valued and included.

Wavicle provides an attractive benefits package for its BigQuery Platform Architect/DBA roles, including comprehensive healthcare, retirement plans, life insurance, disability coverage, and unlimited paid time off. Employees also have access to short-term and long-term incentive plans, which include annual bonuses, contributing to an overall positive work-life balance.

When discussing your experience with BigQuery, focus on specific projects where you've utilized its capabilities. Highlight your proficiency in managing data workflows, performance optimization, and design of scalable data solutions. Provide examples of how you’ve led teams or collaborated with engineers to achieve project goals.

When replying, discuss the security measures you’ve implemented in past roles, such as access management, auditing, and encryption methodologies. Emphasize your awareness of regulatory requirements and how you ensure that compliance protocols are followed while maintaining a user-friendly environment in BigQuery.

Detail strategies like query optimization techniques, indexing, and the proper use of partitioning and clustering. Share specific examples of how you have successfully enhanced performance on previous projects using BigQuery.

Identify a specific challenge, outline the context, and explain the steps you took to resolve the issue. Focus on your analytical approach, the use of BigQuery features to tackle the problem, and the outcomes that positively impacted project deliverables.

Explain your approach to automating ETL processes, including any tools you've used such as Apache Beam or Cloud Data Fusion. Discuss how you’ve streamlined data integration tasks from various sources and the benefits this brought to past projects.

Mention specific tools you've utilized for monitoring and optimization, which could include GCP console monitoring, logging and profiling tools, and any third-party solutions you find effective. Discuss the metrics you track and how this data informs performance improvements.

Discuss your leadership style and previous experiences leading teams. Highlight how you facilitate collaboration among team members and how your guidance has contributed to the success of BigQuery projects.

Share your knowledge and past experience in managing costs related to BigQuery, including strategies for optimizing query costs, using budget alerts, and tracking spending in Google Cloud. Discuss how you strike a balance between cost-effectiveness and performance.

Offer a detailed example of a past project involving data migration to BigQuery. Explain the challenges you faced, the migration strategy employed, and the successful outcomes achieved, particularly focusing on lessons learned.

Elaborate on your familiarity with machine learning processes and how they can be integrated with BigQuery, providing real-world examples. Mention any tools you have experience with, such as BigQuery ML, and how they have enhanced data analysis and modeling capabilities.