Description:
• Architect and productionize batch and real-time data systems to support various products and business needs.
• Ensure the quality, performance, and stability of data systems through robust quality systems and monitoring practices.
• Design and optimize data models for efficient storage and retrieval to meet critical product and business requirements.
• Collaborate with cross-functional teams, including product managers, engineers, data scientists, and business partners, to align on data requirements and develop scalable systems.
• Tune, productionize, and optimize data systems and machine learning models to enhance their effectiveness and efficiency.
• Build and maintain strong relationships with partner engineering teams, including backend, client, data science, and ML teams, to ensure seamless integration and support.
• Contribute to the development of long-term data strategies and roadmaps, and influence the technical direction of data engineering practices within… the organization.
• Mentor and coach team members, providing guidance and support to enhance their skills and performance.
Requirements:
• 9+ years of relevant industry experience with a Bachelor’s and/or Master’s degree in CS/EE, or equivalent experience, or 6+ years of experience with a PhD.
• Extensive experience designing, building, and operating robust distributed data platforms (e.g., Spark, Kafka, Flink, HBase) and handling data at the petabyte scale.
• Strong knowledge of Java, Scala, or Python, and expertise with data processing technologies and query authoring (SQL).
• Proven ability to design, productionize, and optimize batch and real-time data pipelines and systems, ensuring their quality, performance, and stability.
• Excellent ability to collaborate with cross-functional teams, including product managers, engineers, data scientists, and business partners, to align on requirements and drive data-driven decision-making.
• Advanced analytical and problem-solving skills with a focus on data quality, governance, and system reliability.
• Exceptional written and verbal communication skills, capable of influencing stakeholders and conveying complex technical concepts effectively.
• Expertise in data modeling, warehousing, and working with relational (e.g., PostgreSQL, MySQL) and columnar databases (e.g., Redshift, BigQuery).
• Experience working with machine learning engineers to integrate ML models into data systems and products (preferred but optional).
• Ability to provide technical leadership and mentorship, guiding teams on best practices and contributing to the development of data engineering strategies.
• Flexibility and innovative thinking to evaluate and incorporate new technologies and methodologies to improve data processes and solutions.
Benefits:
• Eligible for bonus, equity, benefits, and Employee Travel Credits
