Cloud Engineer III - Databricks, Snowflake, GCP/AWS (Hybrid)

Cloud and data remain a core enabler for several of our enterprise strategies: rounding the corner on Digital Transformation, executing on M&A, driving Investment Programs, and enabling GenAI. The Core Infrastructure Team at FactSet is looking for an experienced Cloud Engineer with deep expertise in Databricks and Snowflake to join our growing Cloud team. The team is comprised of Cloud Architects and Engineers, focusing on overall Cloud Architecture strategy & execution, and enablement of the organization to create and run optimized Cloud Native solutions aligned to our Internal Developer Platform. The Cloud Engineer will help maximize the benefit FactSet receives from its usage of public cloud, with a particular focus on governing, scaling, and optimizing our Databricks and Snowflake platforms. The engineer will directly contribute to the growth of the business by building well-governed cloud data and AI/ML services across FactSet's public cloud partners.

Responsibilities:

• Support the planning and implementation of enterprise data platforms across AWS and GCP

• Work with Core Infrastructure teams to drive adoption of cloud-native services and ensure core infrastructure is easily consumable by engineering teams

• Increase velocity of cloud migration and digital transformation by helping engineering teams adopt best practices around automation and release processes

• Work with the FactSet security organization to ensure a strong security posture of services and data in the cloud

• Administer Databricks Unity Catalog governance across 100+ projects; manage compute policies, cluster custodian rules, and access controls, without granting users account or metastore admin rights

• Maintain and extend the Terraform/Databricks Asset Bundles (DABs) CI/CD pipeline (GitHub Actions + CodeBuild) that deploys notebooks, pipelines, jobs, AI/ML workflows, and project settings

• Help teams manage DDL and grants through Flyway-based migration tooling, keeping Terraform as the single source of truth for access grants

• Support cost and FinOps analysis across Databricks DBU spend, cloud storage (S3), and connected data platforms (e.g., Snowflake external connections)

• Contribute to multi-cloud strategy work, including AWS-to-GCP migration planning for AI workloads

• Design and evaluate secure external and third-party access architectures (e.g., OIDC-based access for large external client bases), balancing scale limits, performance, and security requirements

• Build and operate centrally-managed observability tooling that auto-instruments alerting across all jobs and pipelines, tracking baseline execution duration and scalability to detect performance regressions

• Implement redesign of workspace architecture (currently split by dev/UAT/prod) into a catalog-based structure organized by disaster-recovery tier, in preparation for Databricks' multi-region DR capability; own the eventual DR implementation once that feature is generally available

• Maintain working knowledge of underlying AWS and GCP platform services (networking, IAM, CloudWatch) to support Databricks network connectivity configuration (NCCs) and troubleshoot connectivity and performance issues via cloud-native logs

Minimum Requirements:

• 3+ years of hands-on public cloud experience (AWS and/or GCP) successfully making engineering contributions in a fast-paced, high growth business

• 2+ years of hands-on Databricks experience, including Unity Catalog, Delta Lake, and Databricks Asset Bundles (DABs)

• 1+ years experience with Snowflake, including data warehousing concepts, access management, and cross-platform integrations

• Demonstrable experience working in cross-functional technology teams

• Fluency in English, both written and verbal

Critical Skills:

• 3+ years AWS and/or GCP experience working in a highly distributed fast paced environment, with experience supporting Databricks workloads on cloud infrastructure

• Proven track record of being an innovative and pragmatic technology expert

• Understanding of Cloud Computing technologies

• Strong working knowledge of one or more of the following: Python, Terraform, or Ansible, with specific experience using the Databricks Terraform provider and Databricks Asset Bundles (DABs)

• Experience with Docker, EC2/ASG, Serverless (Lambda), ECS/EKS

• Strong foundational knowledge of Cloud Networking services such as: VPC, TGW, Direct Connect, ALB/NLB, PrivateLink

• Hands-on experience with Databricks Unity Catalog, including governance, access controls, and compute policy management

• Experience with CI/CD tooling (GitHub Actions, AWS CodeBuild) for data platform deployments

• Knowledge of Generative AI and AI/ML tools & techniques, including experience deploying and managing AI/ML workflows on Databricks (e.g., MLflow, Model Serving)

• Experience with observability and monitoring tooling for data pipelines and workflows

• Strong desire for learning new tech and solving challenging engineering problems

Additional Skills:

• Excellent communication skills, with the ability to participate in technology discussions at all levels, including translating complex data platform governance and architecture concepts to non-technical stakeholders

• Ability to review initiatives, understand detailed business processes and technology, and make timely decisive decisions

• Strategic, analytical, and creative thinking style with a realistic, pragmatic approach

• Experience working with FinOps principles, with the ability to analyze and report on cloud and data platform spend (Databricks DBU, S3, Snowflake)

• Familiarity with database migration tooling such as Flyway or Liquibase

• Understanding of identity and access management patterns, including OIDC-based authentication for external client access

• Experience with multi-cloud environments, particularly AWS and GCP, and familiarity with cross-cloud data and AI workload migration

Education:

• Bachelor’s degree in information/computer science, engineering, or related discipline with IT focus required.