Top Senior Level Python Jobs

The Senior Site Reliability Engineer will ensure service reliability, availability, and performance while collaborating with engineering teams. Responsibilities include incident resolution, automation, monitoring setup, documentation, and maintaining secure environments. The role involves participating in on-call rotations and requires experience with Linux systems, container technologies, and cloud providers.

This role involves developing custom plugins and integrations for cybersecurity software, focusing on data models and storage strategies, data transformation, and querying big data.

The Senior Solutions Engineer—Analytics will analyze product features and assist with complex client-related inquiries while providing insights from big data. This role involves collaboration with Product Managers and Data Scientists to create meaningful presentations and support business development projects through data analytics and dashboard development.

The People Analytics Lead will analyze HR data to inform strategic decisions, enhance employee experience, and improve organizational performance. Responsibilities include managing analytics technologies, developing HR dashboards, ensuring data governance, and collaborating with stakeholders for data-driven recommendations.

The Senior Technical Advisor will provide expertise in systems architecture and recommend enhancements for the organization's infrastructure and applications. The role involves evaluating systems, ensuring data integrity, and implementing technology standards while providing guidance to project teams.

The Senior Software Developer will redesign applications and migrate data from Ab Initio to Python and Oracle SQL. This role involves collaborating with internal teams, designing and coding Python applications, and analyzing test results to improve application performance.

The Director of Engineering - Data will lead a team to develop and implement data-driven strategies and advanced analytics solutions. Responsibilities include overseeing data pipeline creation, machine learning models, and adopting new data technologies to enhance products and services, ensuring quality data utilization across various teams.