Staff Data Infrastructure Engineer - AI-Driven Analytics

Slack Enterprise; San Francisco (CA); Híbrido; USD 197.000 - 314.000; Jornada completa

Descripción de la vacante

Slack is seeking a Staff Software Engineer for its Data Infrastructure team. You will design, build, and operate scalable data platforms powering analytics and ML workflows at scale.

You will own core services like Pinot and Trino, while maintaining uptime and performance. Collaboration with data engineers, security, and platform teams is essential, with a focus on cost efficiency and developer experience.

Formación

• 10+ years of software, platform, or infra engineering, with data-intensive systems experience.

Responsabilidades

• Design, build, and operate reliable and scalable data infrastructure powering Slack’s analytics, ML, and data-driven decision-making.
• Serve as DRI for core data services like analytics infrastructure (Pinot, Trino) to ensure uptime and performance.
• Own compute/orchestration services (Airflow, Spark on Kubernetes, EMR, Hive Metastore).
• Drive security, cost efficiency, and developer experience across data infra.
• Build automation and self-service tools for data workflows.
• Collaborate with data engineering, platform, and security teams to scale and govern solutions.
• Improve observability, monitoring, and alerting with Grafana and related tooling.
• Partner with staff engineers to define best practices and support models.
• Mentor and coach engineers to model ownership and operational excellence.

Conocimientos

Big data infra

Python

Golang

SQL

Distributed systems

Educación

Bachelor’s degree in CS/Engineering

Herramientas

GitHub Actions

Vault

Terraform

Kubernetes

Grafana

Airflow

Pinot

Trino

Hive Metastore

EMR

Descripción del empleo

Slack is seeking a Staff Software Engineer for its Data Infrastructure team. You will design, build, and operate scalable data platforms powering analytics and ML workflows at scale.

You will own core services like Pinot and Trino, while maintaining uptime and performance. Collaboration with data engineers, security, and platform teams is essential, with a focus on cost efficiency and developer experience.