Sr. Cloud Data Platform Engineer

About the Role Senior Data Engineer with strong DevOps and cloud infrastructure experience to build and operate the infrastructure and pipelines that support Palantir deployments. This role centers on Terraform-based infrastructure-as-code, AWS data access and governance patterns, especially Lake Formation, and the pipelines that connect AWS-hosted data to Palantir and other analytics/ontology platforms. Key Responsibilities Design, build, and maintain infrastructure-as-codeusing Terraform across AWS environments supporting data platforms. Configure and manage AWS Lake Formation, including tag-based access control (LF-TBAC), to govern data access at scale. Build and maintain DevOps pipelines that integrate AWS data sources with third-party analytics/ontology platforms (Palantir Foundry and similar). Support data pipeline automation, CI/CD, and infrastructure reliability for production data workloads. Apply data governance and security best practices (RBAC, tagging, markings) across the AWS-to-downstream-platform integration. Collaborate with data engineers, analysts, and platform owners to ensure secure, well-governed data access. Document infrastructure patterns and mentor team members on Terraform and AWS best practices. Required Qualifications 5–6 years of professional data engineering / DevOps experience. Strong, hands-on Terraform experience (infrastructure-as-code, modules, state management). Deep AWS experience, with strong understanding of AWS Lake Formation, including tag-based access control (LF-TBAC), and how DevOps pipelines integrate AWS data through Lake Formation with third-party platforms. Strong Python and SQL; comfortable with CI/CD and Git-based workflows. Solid grasp of data governance, access control models, and pipeline automation. Due to ITAR requirements, must be aUScitizen or Green Card holder. Preferred Qualifications Experience with Palantir Virtual Tables or equivalent concepts on other platforms (Snowflake external tables, Databricks Unity Catalog external tables, BigQuery external/federated tables). Interest in developing deeper Palantir expertise over time. Hands-on experience is not required today, but openness to growing into it is a plus. Prior exposure to Palantir Foundry, Snowflake, Databricks, or BigQuery in a production environment. Experience with streaming or near-real-time data processing (Kafka, Spark Streaming).