Do you love automating things and keeping data platforms reliable? We are looking for a skilled DataOps Engineer to join our Data Team and make sure our data platform runs reliably, securely and cost-effectively, helping drive our company toward a more data-driven future.
The role
You will own the operational side of our data platform: infrastructure, deployments, monitoring and costs. Your work will have a direct impact on product development, customer support processes and overall business strategy, because every report, dashboard and AI tool we build depends on the platform you keep running. If you enjoy taking ownership, automating everything you can and working closely with engineers and analysts, this role is an excellent opportunity for you!
What you’ll actually do
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Maintain and automate our cloud data platform (GCP, Databricks), which powers our reporting suite and internal data systems.
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Manage infrastructure as code with Terraform for GCP and Databricks.
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Build and maintain CI/CD pipelines in GitLab, including Databricks deployments with Asset Bundles.
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Maintain Docker images and runtime environments for Dagster and Airbyte, with zero-downtime upgrades.
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Own development and production environments and the release process.
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Set up observability for the platform and pipelines: alerting, metrics, logs and data freshness monitoring.
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Define SLOs for key pipelines, handle incidents, write runbooks and run postmortems.
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Manage access, IAM, service principals and secrets, following the principle of least privilege.
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Monitor and optimize cloud costs (FinOps) across GCP and Databricks.
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Maintain and support the development of Hermes, our AI analytics environment (deployments, BigQuery datasets, monitoring and tracing in Langfuse).
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Support data lineage, cataloguing and governance with DataHub.
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Work with Data Engineers to run their pipelines reliably in production, and with analysts on data availability, SLAs and self-service access.
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Automate repetitive operational work and help retire legacy tools.
→ Tech stack
Databricks, GCP, BigQuery, Terraform, GitLab CI, Docker, Dagster, Airbyte, DataHub, ClickHouse, Power BI, Langfuse, Python, SQL
What we're looking for
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Infrastructure as Code: Hands-on experience with Terraform.
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CI/CD and containers: Hands-on experience with CI/CD pipelines, Docker and Bash.
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Cloud platforms: Hands-on work in a cloud environment, preferably GCP (IAM, networking, cost management).
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Observability: Experience with monitoring, alerting and handling production incidents.
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Orchestration: Experience operating orchestration tools such as Dagster or Airflow.
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Databricks: Working knowledge of Databricks (jobs, clusters, workspace administration).
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Experience: 3+ years of commercial experience with Python and SQL.
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Deployment: Proficiency with deploying and running production-grade applications.
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Communication: Very good communication skills with the ability to work with both technical and non-technical stakeholders.
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Teamwork: Excellent team player who embraces teamwork and mutual support.
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Languages: Fluency in Polish (C1+) and strong command of English (B2+) required.
→ Nice to have
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Background: Previous experience in a DevOps, SRE, Platform Engineering or Data Engineering role.
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Databricks tooling: Experience with Databricks Asset Bundles and Unity Catalog.
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Data tooling: Experience with Airbyte, DataHub or ClickHouse.
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LLMOps: Experience running AI/LLM applications in production (e.g. Langfuse).
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Spark: Commercial experience with Spark.
What comes with the job
Contract terms: B2B cooperation. Monthly compensation range: 23,000–27,000 PLN, depending on experience and the agreed scope of services.
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Remote-first collaboration model
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International technology and product projects
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Flexible project collaboration setup
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Planned service breaks aligned with contract terms
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Opportunity to collaborate with experienced product and technology teams
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Opportunity to access selected knowledge-sharing initiatives
→ Recruitment process
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Intro call with the Recruiter (up to 45 minutes).
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Technical interview with Hiring Manager and Data Engineer (up to 60 minutes).
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Team fit interview with Hiring Manager and Data Analyst (up to 60 minutes).
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