Title: Principal Data Engineer
Duration: 12 Months
Location: Remote
As a Principal Data Engineer you will design, build, and maintain scalable data pipelines and models that power critical data products. These include centralized user data models, analytics for digital content and live events, customer support insights, and activity data systems. These systems support understanding of user onboarding funnels, engagement patterns, support efficiency, and content effectiveness. You will collaborate with product managers, analysts, and engineers to ensure data infrastructure is reliable, performant, and directly tied to delivering better user experiences.
Responsibilities:
Design, build, and optimize scalable ETL pipelines for structured and semi-structured data supporting insights use cases, growth metrics, live event analytics, and other external-facing data sets.
Design and implement data models using industry best practices that capture a complete ecosystem view of user experiences, ensuring accuracy, scalability, and long-term usability.
Architect and implement robust, maintainable, and high-performance data solutions.
Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for content, growth, and live event analytics.
Mentor data engineers across all levels, setting best practices and fostering technical growth.
Optimize query performance and resolve pipeline bottlenecks to improve data accessibility.
Evaluate and adopt new tools, frameworks, and methodologies to advance data engineering capabilities.
Support cost optimization by ensuring scalable and efficient data solutions.
Ensure data quality, governance, and compliance with regulatory standards (e.g., GDPR, CCPA).
Contribute to the organization s data engineering discipline by shaping infrastructure, standards, tooling, and best practices.
Required Qualifications:
Bachelor's or Master s degree in Computer Science, Information Systems, Engineering, or a related field.
8+ years experience in data engineering or a related field.
Strong Expertise in big-data technologies, including Python, GoLang, Spark, Scala, SQL, and tools like Airflow and dbt.
Proficiency in cloud infrastructure (AWS or Google Cloud Platform) and Databricks.
Expertise in designing efficient and scalable data models with large data sets across multiple teams and in collaboration with insights or data stakeholders.
Demonstrated ability to work cross-functionally with engineering, analytics, and product teams.
Proven experience mentoring and guiding other engineers.
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