Senior Data Engineer

Responsibilities

• Design, develop, and maintain high-performance, product-centric data pipelines using Airflow, dbt, and Python.
• Architect and optimize a massive-scale data warehouse and lakehouse, primarily using Snowflake.
• Integrate structured and unstructured data sources, including web data and third-party APIs, into the data ecosystem.
• Define roadmap priorities for internal data consumers and data and AI capabilities.
• Advise leadership on strategy, AI readiness, and data infrastructure investments.
• Collaborate with ML engineers, data scientists, and product managers to deliver scalable data solutions.
• Define, monitor, and enforce data quality SLAs, including data accuracy and lineage.
• Participate in a shared PagerDuty on-call rotation, respond to incidents, perform root-cause analysis, and drive remediation and postmortems.
• Triage production issues and escalate based on severity, blast radius, and customer impact.
• Make sound decisions amid ambiguity and iterate with stakeholders.
• Mentor junior engineers and promote best practices in code quality, data architecture, incident response, and operational excellence.
• Participate in architectural decisions and long-term enterprise data infrastructure strategy, focusing on cost, performance, reliability, and observability.
• Maintain runbooks, on-call documentation, and operational playbooks.

Requirements

• Expert-level SQL for performant, scalable queries and transformations on massive datasets.
• Strong Python programming skills, including distributed computing, data manipulation, and robust API development.
• Production experience with large-scale batch and streaming data processing.
• Hands-on experience with dbt for advanced data modeling and transformations.
• Deep knowledge of Snowflake data warehouse design, optimization, and cost modeling.
• Experience owning production systems, including on-call rotations, incident response, and postmortems.
• Understanding of data lakes, event-driven architectures, Kafka, ETL/ELT, and data mesh.
• Proficiency with GCP and/or AWS and infrastructure as code such as Terraform.
• Experience with monitoring and observability tools such as Datadog, Monte Carlo, and Grafana.
• Familiarity with CI/CD practices for data workflows, automated pipeline testing, and version-controlled data models.
• Excellent communication and stakeholder management skills.
• Strategic, product-oriented thinking and the ability to translate business needs into scalable data solutions.
• Leadership, mentorship, ownership, accountability, sound judgment, and strong documentation habits.
• Bachelor's or master's degree in Computer Science, Engineering, or a related field.
• 8+ years of progressive data engineering experience.
• Experience implementing or scaling data infrastructure for a data-centric product company.
• Experience participating in an on-call rotation supporting production data systems.

Compensation and Benefits

• Compensation and benefits details are not specified in the provided posting.