Staff Data Engineer - Enterprise Platform, AI foundations
About this role
Professionals who are relentlessly curious, committed to continuous learning, and eager to tackle petabyte-scale data challenges will find a natural home in this role. SentinelOne seeks a Staff Data Engineer to build, scale, and modernize the enterprise data platform, focusing on modern data engineering, analytics enablement, governance, and AI-ready data foundations.
In this position, the engineer will own and improve the trusted data foundation supporting executive reporting, analytics, revenue operations, product insights, customer reporting, finance, GTM, and operational workflows across the company. Working closely with technical and business teams, the chosen candidate will shape how data engineering scales within an AI-enabled enterprise environment, offering meaningful platform ownership and broad business visibility.
What you will do
- Build, maintain, and troubleshoot production-grade data pipelines across source systems, cloud data platforms, and business-facing data products involving petabytes of data.
- Support and improve critical data workflows using BigQuery, GCP, Kestra, Nexla, DataHub, Looker, Tableau, and Workato across Salesforce, billing, ARR, customer data, and finance domains.
- Improve data quality, observability, freshness monitoring, schema validation, reconciliation, alerting, and operational reliability for critical datasets.
- Participate in production support for business-critical data workflows, including incident response, root-cause analysis, and runbook development.
- Strengthen data governance and discoverability through metadata management, lineage, ownership, certification, and metric definitions.
- Partner with Analytics, BI, Product, Finance, GTM, IT, Security, and Engineering stakeholders to deliver trusted datasets, AI-ready data foundations, and scalable data solutions.
- Leverage AI tools responsibly to increase engineering productivity, including SQL support, Python development, debugging, testing, and operational analysis.
What you need
- Demonstrate advanced SQL and Python proficiency for building, optimizing, troubleshooting, automating, and testing data pipelines across large datasets.
- Bring proven experience building and supporting batch or near-real-time ingestion pipelines using tools such as Nexla, Workato, Fivetran, APIs, or custom pipelines.
- Possess hands-on experience with a cloud-based data stack such as GCP and BigQuery or comparable alternatives in the AWS ecosystem.
- Maintain experience with workflow orchestration tools such as Kestra, Airflow, dbt Cloud, or similar technologies.
- Apply a deep understanding of dimensional modeling and data quality practices like reconciliation, schema validation, and freshness monitoring.
- Show familiarity with metadata management, lineage, data catalogs, semantic layers, and engineering best practices including Git, CI/CD, testing, and code reviews.
Benefits
- Restricted Stock Units (RSUs) and Employee Stock Purchase Plan (ESPP)
- Flexible time off on top of the standard 5 weeks PTO
- Private medical care benefit, life insurance, and disability insurance
- Global home office allowance and meal allowance
- Wellbeing allowance, MultiSport benefit program, and Wellness Coach app
- Hybrid work in Prague or Brno, or remote work in CZ/SK
Skills and tech stack
Questions candidates ask
What are the location and remote work options for this role?
The position supports hybrid work in Prague or Brno, or remote work in the Czech Republic or Slovakia. Only Prague-based employees are required to work from the office at least 2 days per week.
What tools and technologies will I be working with?
You will work with a modern tech stack that includes BigQuery, GCP, Kestra, Nexla, DataHub, Looker, Tableau, and Workato across Salesforce, billing, customer data, and finance domains.
What kind of scale will the data pipelines operate at?
You will be tasked with building, maintaining, and troubleshooting production-grade data pipelines where you will deal with datasets at petabytes scale.