SharkNinja is hiring a
Lead Analytics Engineer

Summary

Join our team as a Lead Analytics Engineer and spearhead the development and optimization of our data analytics frameworks and infrastructure. As lead engineer, you will lead the development across one of 3 organization pillars (finance, ops or revenue). You will partner closely with the leads of each pillar to drive consistency and reusability of warehouse objects.

Requirements

• 3-5 years of proven experience as a Lead Analytics Engineer or similar role, with a strong portfolio of successful data projects
• Expert knowledge of SQL and database management systems, particularly Snowflake
• Extensive experience with ETL methodologies and data modeling
• Proficient in orchestration tools like Airflow and Dagster
• Hands-on experience with GIT for version control
• Programming skills in Python and familiarity with data pipeline development are highly desirable
• Excellent problem-solving skills and the ability to work independently or as part of a team
• Strong communication skills, both verbal and written
• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field
• Advanced degrees or professional certifications related to data engineering are preferred

Responsibilities

• Mentor members of the analytics engineering team, providing technical guidance, and support on code and projects
• Foster a collaborative and innovative team environment, encouraging professional growth and development
• Define and execute the analytics engineering strategy, aligning with organizational goals and business needs
• Identify opportunities for process improvements and implement best practices in data analytics and engineering
• Oversee the design, development, and maintenance of scalable and efficient data models using DBT and Snowflake
• Manage multiple projects simultaneously, ensuring timely delivery and adherence to high-quality standards
• Collaborate with cross-functional teams to gather requirements and deliver data-driven solutions and insights
• Develop and optimize SQL queries and scripts for data transformation and aggregation
• Implement and manage ETL processes, ensuring data accuracy and integrity
• Lead the design and implementation of data orchestration workflows using tools such as Dagster or Airflow
• Build out automated AI workflows using LLMs like ChatGPT, integrating advanced AI capabilities into data processes
• Ensure compliance with data governance and security policies
• Work closely with data scientists, analysts, and other stakeholders to support data modeling and analysis efforts
• Communicate effectively with technical and non-technical stakeholders, translating complex technical concepts into actionable insights
• Champion the use of GIT for version control and collaborative development
• Promote a culture of continuous learning and improvement within the analytics engineering team