Staff Data Infra Engineer - AI-Driven, Scalable

Slack; Atlanta (GA); Sur place; USD 197 000 - 345 000; Plein temps

Résumé du poste

Slack is seeking a Staff Software Engineer to lead the Data Infrastructure team. You will design, build, and operate scalable data platforms powering analytics, ML, and data‑driven decisions at scale.

You will act as DRI for core services (e.g., Pinot, Trino) and drive improvements in reliability, security, and cost efficiency, while mentoring engineers and advancing observability and automation.

Qualifications

• 10+ years in software, platform, or infrastructure engineering.
• Proven ability to build, deploy, and operate distributed infra at scale.
• Strong background with big data and infra tech such as EMR, Airflow, Pinot, Trino, Hive Metastore, Kubernetes.

Responsabilités

• Design, build, and operate reliable data infrastructure powering Slack’s analytics, ML, and data‑driven decision‑making.
• Serve as DRI for multiple core data services like Pinot and Trino; ensure uptime and reliability.
• Drive security, cost efficiency, and developer experience improvements across data infra.
• Build automation and self‑service tools for data workflows.
• Collaborate with data engineering, platform, and security teams to design scalable, well‑governed solutions.
• Enhance observability, monitoring, and alerting with Grafana and related tooling.
• Mentor engineers and model ownership and operational excellence.

Connaissances

10+ years exp

Cross-functional collab

Distributed infra

Big data tech

Python

Golang

SQL

Bash

Formation

BSc in CS/Engineering

Outils

EMR

Airflow

Pinot

Trino

Hive Metastore

Kubernetes

GitHub Actions

Vault

Terraform

Grafana

Description du poste

Slack is seeking a Staff Software Engineer to lead the Data Infrastructure team. You will design, build, and operate scalable data platforms powering analytics, ML, and data‑driven decisions at scale.

You will act as DRI for core services (e.g., Pinot, Trino) and drive improvements in reliability, security, and cost efficiency, while mentoring engineers and advancing observability and automation.