Operational instinct with a strong respect for production systems
3+ years in cloud infrastructure, SRE, or platform engineering
Knowledge of High Availability architecture principles
Experience with workflow orchestration tools like Airflow
Strong Linux fundamentals and proficiency in scripting languages
Familiarity with distributed data processing frameworks
Experience with containerization and orchestration technologies
Responsibilities
Own the reliability and availability of the data platform across all environments
Enforce and improve discipline around environment promotion
Define and uphold standard operating procedures for deployments
Instrument and monitor platform health using observability tools
Participate in architecture discussions and provide critical feedback
Collaborate with cross-functional teams to identify infrastructure needs
Identify and mitigate reliability risks proactively
Support various systems with an emphasis on stability
Benefits
Equity compensation
Health insurance coverage for employees and their 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 in the office
Team-building events
Hybrid remote work policy with 2 days in office per week
Full Job Description
This is a systems and infrastructure position first. As a Data Platform Engineer, you will be responsible for the reliability, stability, and operational health of our data platform - including how it is deployed, monitored, maintained, and promoted across environments. Data engineering skills are a plus and will be developed on the job; what we cannot teach is operational discipline.
If you have spent your career keeping production systems alive, know what it feels like to break prod and never want to do it again, and treat lower environments as non-negotiable gates rather than suggestions - we want to talk to you.
This is not a data engineering role. You will not spend most of your time writing jobs or consuming the platform. You will be administering, scaling, hardening, and evolving it.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
QualificationsThe 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)
