Staff Software Engineer

As a Staff Software Engineer at Glassdoor, you'll have a major impact on our data platform development, focusing on software best practices, mentorship, and code reviews. You'll ensure our technical architecture supports scalability and performance while promoting a culture of continuous improvement across various teams.

To be a successful applicant for the Staff Software Engineer position at Glassdoor, you should possess over 8 years of experience in software engineering or a similar role, strong skills in cloud technologies like AWS, and a solid understanding of data architectures and machine learning practices.

Glassdoor fosters a culture of growth by providing mentorship opportunities, a collaborative environment where you can share knowledge, and access to resources that enhance your technical and leadership skills, equipping you for success in your career.

For the Staff Software Engineer role at Glassdoor, familiarity with container orchestration tools like Docker and Kubernetes, cloud infrastructure (AWS services), and big data tools is essential. Also, hands-on experience with programming languages such as Python and Java will be beneficial.

The team culture at Glassdoor for the Staff Software Engineer position thrives on collaboration, open communication, and a proactive approach to problem-solving. We value diversity, encourage innovation, and support one another to meet ambitious goals together.

As a Staff Software Engineer at Glassdoor, you can expect a fantastic work-life balance with flexible hours, an open paid time-off policy, and the option to work remotely, allowing you to manage your personal and professional responsibilities effectively.

Glassdoor offers comprehensive benefits to Staff Software Engineers, including 100% employer-paid health premiums, generous stock options, a robust 401(k) plan, wellness programs, and various paid leave options, ensuring employee health and financial security.

To ensure data quality, I prioritize establishing a robust data quality strategy that includes thorough testing, regular audits, and automating data validation processes. By implementing these measures, I can help prevent poor data from entering production.

I successfully led a project where we overhauled an existing data infrastructure to make it scalable. By collaborating with cross-functional teams, conducting architecture reviews, and implementing advanced data processing techniques, we significantly improved performance and reduced operational overhead.

I focus on creating a supportive and collaborative environment for mentoring junior engineers. Regular one-on-one sessions, pairing on coding tasks, and providing constructive feedback help to nurture their skills and foster their growth.

My experience with AWS spans a variety of services, such as EC2, S3, and Lambda. I've worked on deploying applications, managing infrastructure as code with Terraform, and optimizing resource usage for performance and cost efficiency.

For effective CI/CD in big data applications, I implement automated testing environments using tools like Jenkins and GitHub Actions. This ensures code quality and streamlines deployment processes, allowing timely updates without disrupting services.

To stay updated, I follow industry blogs, attend conferences, and participate in online communities. Engaging with other professionals allows me to exchange knowledge and learn about emerging technologies and best practices.

My code review process involves reviewing for clarity, performance, maintainability, and adherence to best practices. I provide constructive feedback, encourage discussions, and aim to create a learning experience for everyone involved.

Balancing technical debt and feature delivery involves prioritizing the most impactful maintenance tasks alongside new feature development. I advocate for a dedicated sprint for technical debt in our planning discussions to ensure it’s consistently managed.

I typically use tools like Apache Spark for large-scale data processing, along with Airflow for orchestrating complex workflows. This combination allows for robust data transformations while maintaining scheduling and monitoring capabilities.

In a previous role, I needed to persuade stakeholders to invest in upgrading our data pipeline. By presenting data-driven analyses, showcasing potential efficiencies, and fostering discussions about long-term benefits, I successfully garnered the necessary support.