Top Senior Level Data & Analytics Jobs

As a Senior Machine Learning Engineer at Wasabi, you will develop and refine computer vision algorithms and NLP models while leading AI advancements in a team-oriented environment. Responsibilities include collaborating across teams, optimizing machine learning models, and ensuring reliable implementation and maintenance of AI systems.

The Senior Cloud Data Engineer will design, develop, and enhance data solutions and pipelines, driving data-driven decisions. Responsibilities include collaborating with teams, building cloud-native solutions, optimizing data pipelines, and mentoring junior engineers.

The Senior Data Engineer will lead data engineering projects, including building ETL tools, data pipelines, and enhancing data quality and governance for a real estate data platform. This role involves collaboration with cross-functional teams and shaping team culture.

As a Senior Data Scientist in Engine Security at Roblox, you will analyze and improve security measures against malicious behavior, assessing the impact of product launches and developing methodologies for evaluating player loss. Collaborating with engineering and program management teams, you'll elevate data practices and address security vulnerabilities.

The Principal Analyst - Executive Initiatives role at Atlassian involves engaging in strategic planning and sales operations, leveraging a data-driven approach to drive impactful business decisions. Responsibilities include project contribution, strategic analysis, insight generation, and cross-functional collaboration, focusing on revenue growth and customer metrics enhancements.

As a Senior Analytics Engineer II, you'll transform raw data into structured datasets, lead data ingestion workflows using Fivetran, and design data pipelines in Snowflake using dbt. You'll monitor workflow health with Dagster and implement best practices for data validation while ensuring compliance with security regulations.

The Staff Machine Learning Engineer will design, build and productionize machine learning models to tackle business challenges, collaborate with product managers and engineers, maintain high code quality, and research emerging technologies. They will also share knowledge through presentations and discussions.