Top Senior Level Data Engineer Jobs

The Sr. Software Engineer will develop and maintain data and analytics solutions for Paze, focusing on building data engineering solutions for batch and stream processing, as well as advanced analytic solutions using BI and ML tools. Responsibilities include collaborating with teams, ensuring quality and performance, and troubleshooting production issues.

As an AI Research Scientist focusing on engineering and operations, you will bridge theoretical research and practical implementation of AI technologies. Your work will involve developing machine learning models, managing server infrastructure, writing code for AI applications, and collaborating with cross-functional teams to generate actionable insights from complex data sets.

As a Senior Data Engineer, you will design and optimize data pipelines, ensuring data consistency and reliability. Collaborating with product and engineering teams, you'll enhance data solutions for product services, drive best practices, and participate in on-call rotations to maintain system reliability.

As a Staff BI & Analytics Data Architect, you will modernize BI technology, manage offshore teams, design data platforms, and collaborate with business departments to develop data solutions, enabling self-service analytics and advanced data analysis.

The Senior Data Architect designs and maintains data architecture and models for various integration projects, ensuring data quality and governance. Responsibilities include optimizing ETL processes, mentoring junior architects, and providing end-user training for the EDW.

The Head of Data Operations will oversee global data operations ensuring data accuracy, availability, and reliability. This role includes managing an international team, overseeing data transfers/services, incident management, and optimizing operational workflows while responding to dynamic priorities.

The Sr. Data Quality Advisor will enhance enterprise decision-making through data quality analysis, evaluating systems like MDM and Azure for data quality gaps, and guiding data cleansing efforts while establishing quality standards and controls.