Staff Data Platform Engineer

Responsibilities

• Own the reliability and availability of our data platform infrastructure across all environments
• Enforce and improve environment promotion discipline — staging is not prod, and prod is sacred
• Define and uphold SOPs around deployments, maintenance windows, and change management
• Instrument and monitor platform health using observability tooling; build alerting that means something
• Participate in architecture and deployment discussions; push back when something isn't ready
• Collaborate with data scientists, engineers, and product managers on infrastructure needs — as a partner, not an order-taker
• Identify and remediate reliability risks before they become incidents
• Support customer-facing and internal systems with a bias toward stability over velocity

Qualifications

The right candidate leans SRE. Data platform experience is additive — we will train the right person. Bullets marked with * are strongly preferred; all others are meaningful signal.

• Operational instinct — "the fear" — you've been burned by prod, you respect it, and you've built habits around it. You know what a proper maintenance window looks like, you communicate before you touch production, and you don't spin up new initiatives while something critical is still burning in.
• 3+ years in cloud infrastructure, SRE, or platform engineering (AWS preferred; GCP/Azure experience translates)
• High Availability architecture: blue/green deployments, data replication, load balancing
• Experience with workflow orchestration (Airflow or similar DAG-based schedulers — or general job scheduling/cron systems at scale)
• Strong Linux fundamentals and scripting (Bash, Python, or similar)
• Distributed data processing (Spark, PySpark, or similar big data frameworks — or experience managing clusters that run them)
• Containerization and orchestration (Kubernetes, Docker, or similar)
• Data ingestion, ETL, or streaming systems (Kafka, Flink, or similar — or experience operating message queues and pipelines)
• Infrastructure-as-code and provisioning (Terraform, Helm, or similar)
• OLAP and OLTP databases (Clickhouse, Postgres, Redshift, or similar — query patterns, indexing, and operational care)
• Monitoring, logging, and observability (Datadog, Prometheus, Kibana, or similar)
• Managed data platforms (Databricks or similar — administering and scaling, not just consuming)
• Network infrastructure fundamentals: load balancers, DNS, auto-scaling, multi-region topologies, proxies
• Security and access management: least-privilege, secrets management, controls for data systems
• MLOps concepts or tooling — a plus

What we value above technical skills

We are explicitly willing to trade depth in data tooling for the right operational character. Specifically:

• Humility — you don't know everything, you say so, and you ask before acting in unfamiliar territory
• Methodical execution — you minimize variables, you don't premature-optimize, you finish what you started before starting something new
• Communication — you tell the team what you're doing before you do it, especially in shared or production environments
• Ownership — when something goes wrong, you look inward first
• Independence – you can drive projects end-to-end, from ambiguous requirements to high quality deliverables. But you aren’t afraid to ask for help.

Benefits:

• Total compensation ($190,000 - $240,000)
• Equity compensation
• Health insurance coverage for you and your dependents
• 401K, FSA, and commuter benefits
• $150 monthly spending account
• $1,000 annual continued education benefit
• $500 Newbie Productivity Perk
• Unlimited PTO and sick days
• Monthly Company Wellness Day Off
• Snacks, drinks, and catered lunches at the office
• Team building events