Senior DevOps Engineer

We are an AI-native data and technology partner for private capital and healthcare. Founded in 2010 and headquartered in Warsaw, we work with leading PE firms, VC funds, and healthcare organizations to build proprietary data infrastructure, deploy AI solutions, and drive AI-native transformation.

Our clients manage a cumulative $210B+ in assets. Our average engagement runs five years. Our NPS sits above 80.

We have restructured our own company around AI — tools, policies, roles, delivery models, and we are hiring engineers to run this for others.

The project is for an American private equity and investment management fund listed on the Forbes 500, based in New York, supporting data pipeline, infrastructure, and data engineering teams in finance, loans, investments, and real estate.

• 10+ years in IT, including 5+ years in a DevOps/Cloud role
• Deep AWS expertise (IAM, EC2, S3, EKS and beyond)
• Azure awareness for Copilot-adjacent work
• Solid Terraform background with end-to-end ownership of IaC
• Kubernetes experience including setup, scaling, and day-2 operations
• Docker proficiency
• Strong Python and Bash skills
• Identity and auth engineering experience: IAM, Entra ID, OAuth/OIDC, SSO, Okta, Active Directory
• Observability and SRE mindset with runbook writing and reliability ownership
• Experience with relational and NoSQL databases
• C1+ English proficiency (written and spoken)

Nice to have:

• Experience with data pipeline tools (Airflow, Snowflake, Redshift, Athena)
• Familiarity with Artifactory or similar repository managers
• Experience in secure/corporate networking environments (proxies, firewalls)
• Python data stack knowledge: Pandas, NumPy, SQLAlchemy; basic ML libraries a plus
• Comfort working in both Windows and Unix environments

• Designing, building and maintaining cloud infrastructure across AWS environments, with Azure involvement for Copilot integration
• Writing and owning Infrastructure-as-Code (Terraform) from greenfield builds to ongoing refactors
• Running containerized workloads (Docker, Kubernetes) across multi-region setups
• Owning observability: logging, metrics, dashboards, reliability targets, operational runbooks
• Engineering identity and auth solutions including IAM policies, Entra ID, OAuth/OIDC, SSO, Okta, token ownership, service vs. user auth, cross-platform access
• Debugging real production issues such as API failures, auth loops, rate limits, model config problems, client SDK bugs, capacity constraints
• Building reusable tooling and infrastructure for data scientists and engineers
• Supporting the broader data engineering team with DevOps expertise
• Documenting built solutions clearly and thoroughly