Senior Python Engineer - JT

As a Senior Python Engineer at Gorilla Logic, your primary responsibilities will include maintaining and optimizing existing systems on Kubernetes, troubleshooting production issues, managing Kafka pipelines, and ensuring the smooth operation of Elasticsearch and Apache Pinot clusters. You will play a key role in incident management and data orchestration, actively identifying risks and performance bottlenecks while collaborating with various teams.

To qualify for the Senior Python Engineer position at Gorilla Logic, you need a minimum of 5 years of software engineering experience, particularly in system maintenance or reliability roles. You should be proficient in Go and Python, have hands-on experience with Kubernetes, Kafka, and data orchestration tools. A solid understanding of distributed systems and the ability to troubleshoot complex issues is also crucial.

A typical day for a Senior Python Engineer at Gorilla Logic involves monitoring system performance, troubleshooting issues within Kubernetes-based microservices, and collaborating with teams to resolve technical challenges. You'll also spend time optimizing Kafka pipelines, maintaining Elasticsearch clusters, and documenting processes while identifying areas for improvement in our systems.

In this role, a Senior Python Engineer will engage with technologies such as Kubernetes, Kafka, Elasticsearch, and Apache Pinot. You will also work with data orchestration frameworks like Prefect, helping to streamline and enhance workflows while focusing on system reliability and performance.

Gorilla Logic is committed to the continuous growth of its Senior Python Engineers by providing opportunities for training in the latest technologies, encouraging participation in conferences, and fostering a collaborative environment where knowledge sharing is paramount. There’s a strong emphasis on cross-functional teamwork and addressing technical debt, ensuring that you are constantly learning and leading in your field.

When troubleshooting a production issue, start by gathering detailed information about the problem, including logs and error messages. Establish a hypothesis about the root cause based on available data, then reproduce the issue in a controlled environment if possible. Collaborate with team members to share insights and brainstorm solutions, and finally implement a fix while documenting the process to avoid similar issues in the future.

In my previous roles, I’ve extensively utilized Kubernetes for container orchestration, focusing on deployment, scaling, and management of applications. I’ve configured Kubernetes clusters, created Helm charts for deployments, and implemented strategies for service discovery and load balancing among microservices, all to improve system reliability and performance.

Certainly! In a previous role, I dealt with performance degradation in our Elasticsearch cluster. I conducted a thorough analysis of the indices and query patterns, conducted optimizations like adjusting the refresh interval, and fine-tuned mappings. Subsequently, the response time improved significantly, and I documented the changes made for future reference.

To optimize Kafka pipelines, I focus on adjusting configurations such as batch size, compression types, and managing partitions effectively. Monitoring performance metrics through tools can help identify bottlenecks, allowing for targeted optimizations in processing throughput and ensuring reliable message delivery.

I have hands-on experience with data orchestration tools like Prefect and Airflow. I utilized these tools to design and manage complex workflows, ensuring smooth data pipeline execution, dependency management, and retries for failed tasks, ultimately enhancing the efficiency of our data handling processes.

Distributed systems design principles involve ensuring scalability, fault tolerance, and high availability. Key concepts include understanding data consistency versus availability, implementing robust communication protocols, and designing for eventual consistency. In practice, I prioritize creating systems that can withstand failures while efficiently managing resources.

To ensure my code is maintainable and scalable, I adhere to coding standards, use clear naming conventions, and create thorough documentation. Additionally, I implement modular designs that facilitate ease of testing and future scaling. Regular code reviews and refactoring sessions also reinforce maintainability in collaboration with the team.

I commonly use tools like Prometheus and Grafana for monitoring system performance. They provide valuable insights into metrics and logs, allowing me to proactively identify and address potential issues before they escalate, ensuring high system availability.

I have extensive experience with AWS, particularly in deploying and managing scalable applications using services like EC2, S3, RDS, and Lambda. I have also utilized GCP for machine learning projects, leveraging tools like BigQuery and Cloud Functions to provide analytics and streamline processing.

I believe in maintaining clear and concise documentation by using structured templates that outline processes step by step. For operational runbooks, I include key information, common issues, and resolutions, making it easy for team members to follow. This ensures that our knowledge base grows and is accessible for everyone involved.