Software Engineer, Backend - job 1 of 2

As a Software Engineer, Backend at Tatari, you will be responsible for building, managing, and optimizing data infrastructure, designing data pipelines for data ingestion and retrieval, and ensuring the reliability and scalability of our data systems. Your role will also involve collaborating with cross-functional teams, mentoring junior engineers, and identifying performance bottlenecks within our data systems.

To qualify for the Software Engineer, Backend position at Tatari, you should have at least 5 years of experience in data architecture and engineering, and 2 years working with modern data stacks such as Kafka and Spark. Proficiency in programming languages like Python or Java, as well as experience with cloud platforms like AWS is also essential to succeed in this role.

Tatari employs a comprehensive range of technologies for its data engineering processes, including tools like Kafka, Spark, and Airflow, alongside cloud data warehouses such as RedShift and Snowflake. Familiarity with both relational and NoSQL data stores and cloud services like Amazon S3 and Lambda is also highly beneficial.

The Software Engineer, Backend role is crucial to Tatari's mission as it directly involves developing data pipelines that provide accurate metrics for our clients. By ensuring that data infrastructure is effective and scalable, you will enable businesses to create precise marketing strategies that impact their advertising efforts on TV.

Working as a Software Engineer, Backend at Tatari comes with a competitive compensation package, including equity compensation, comprehensive health insurance, unlimited PTO, and team-building events. You will also enjoy perks like a monthly work-related stipend, education reimbursement, and a hybrid work model that allows for both in-office and remote work arrangements.

When answering this question, highlight specific projects where you designed and built data pipelines, detailing the technologies used and the outcomes. Emphasize your understanding of data ingestion, transformations, and delivery, showcasing your ability to support large-scale data operations.

Discuss your strategies for analyzing performance bottlenecks and your methods for tuning queries and indexing. Provide examples where you improved system efficiency and any tools you utilized, making sure to connect your approach to achieving scalable solutions for the business.

Be prepared to dive into your hands-on experience with technologies like Kafka and Spark. Describe how you have leveraged these tools for real-time data processing and the impact of your work on improving data workflows, along with any challenges faced and how you overcame them.

Discuss the monitoring practices you implement to maintain data system reliability. Provide examples of how you proactively identify issues and your experience in using monitoring tools to assure systems are performing optimally.

Share a specific instance that illustrates your mentorship style, how you tailored your guidance to your mentee's needs, and the outcomes of your efforts. Highlight the importance of fostering a collaborative environment and the skills you focus on developing.

This is a chance to showcase your commitment to continuous learning. Talk about any communities, blogs, or courses you follow to keep up with innovation in data engineering, and how you've applied new knowledge to your work.

Identify specific experiences and skills that directly relate to Tatari's mission in TV advertising, such as improving data-driven decision-making or enhancing operational efficiencies. Make the connection clear between your background and how it aligns with Tatari's goals.

Highlight a specific project that required you to overcome significant obstacles, detailing your role and the strategies you employed. Emphasize lessons learned and how they can be applied to future projects at Tatari.

Discuss your preferences for backend programming languages or frameworks and support them with examples of how they have enhanced your productivity or the quality of your work. Be specific about the context in which each language or framework worked best for you.

Explain your approach to communication and collaboration when working with teams outside of engineering, such as data scientists or marketing. Highlight your experience in translating technical jargon into actionable insights for non-technical stakeholders.