Staff Data Engineer - Enterprise Platform, AI foundations
S
SentinelOneCybersecurity AI
Hybrid work in Prague (Karlin), Brno (Clubco) or remote in CZ/SK.Full-TimeStaff
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLGCPData engineeringCI/CDBigQuery
Requirements
- Advanced SQL and Python proficiency for building, optimizing, troubleshooting, automating, and testing data pipelines across large datasets.
- Proven experience building and supporting batch or near-real-time ingestion pipelines using tools such as Nexla, Workato, Fivetran, APIs, or custom pipelines.
- Hands-on experience with Google cloud data stack, including BigQuery, production dataset management, performance tuning, IAM, service accounts, and dev/prod environment separation.
- Experience with workflow orchestration tools such as Kestra, Airflow, dbt Cloud, or similar, including scheduling, dependencies, retries, and failure handling.
- Deep understanding of dimensional modeling (facts/dimensions, SCDs) and data quality/reliability practices (reconciliation, schema validation, freshness monitoring, runbooks).
- Familiarity with metadata management, lineage, data catalogs, semantic layers, and engineering best practices (Git, CI/CD, testing, code reviews).
Responsibilities
- Build, maintain, and troubleshoot production-grade data pipelines across source systems, cloud data platforms, and business-facing data products, where you'll deal with datasets at petabytes scale.
- 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, metric definitions etc.
- 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.
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