Key Responsibilities Technical Leadership & Architecture - Design, build, and deploy end-to-end data integration pipelines using Palantir Foundry, including complex transforms, incremental pipelines, and multi-source data connectors
- Architect and maintain the Ontology layer — defining object types, link types, action types, and interfaces that model customer domains with precision and scalability
- Develop and optimize Python, SQL, and Java transforms across distributed (PySpark) and lightweight (Pandas, Polars, DuckDB) compute engines
- Build and deploy TypeScript/Python Functions for server-side business logic, function-backed actions, and function-backed columns
- Create and configure Workshop applications, OSDK-based custom applications, and custom widgets to deliver operational front-ends
- Lead data governance implementation — markings, permissions, restricted views, property security groups, and role-based access controls
- Design and deploy AIP-powered workflows, including AIP Logic, AIP Agents, evaluation suites, and retrieval-augmented generation (RAG) pipelines
Deployment Strategy & Customer Engagement - Serve as the primary technical point of contact for senior customer stakeholders, translating business problems into platform solutions
- Own deployment roadmaps end-to-end: scoping, architecture design, implementation, testing, and operationalization
- Drive adoption by delivering high-quality, production-grade solutions that users trust and rely on daily
- Navigate complex organizational dynamics, building relationships with executives, analysts, data engineers, and end users
- Identify expansion opportunities by deeply understanding the customer''s operational landscape and surfacing new use cases for the platform
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| Skills: | Team Leadership & Mentorship - Lead and mentor a team of Forward Deployed Engineers, providing technical guidance, code reviews, and architecture direction
- Establish engineering best practices across the deployment: branching strategies (global and local), incremental pipeline design, testing, and CI/CD
- Conduct knowledge-sharing sessions and internal trainings on Foundry capabilities, new platform features, and deployment patterns
- Contribute to DaVita''s internal engineering culture through tooling improvements, documentation, and cross-team collaboration
Platform Mastery & Innovation - Stay at the forefront of Foundry platform evolution — including Pipeline Builder, Code Workspaces, Model Studio, OSDK, Cipher, and streaming capabilities
- Build and deploy machine learning models using Model Studio (no-code) and pro-code repositories for classification, regression, time series forecasting, and custom predictive modeling
- Leverage the full data lifecycle: Data Connection → Sync → Datasets → Transforms → Ontology → Applications → Actions → Writeback
- Partner with Palantir product teams and DaVita''s internal Palantir development team to provide field feedback and shape the future direction of the platform
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| Education: | Required Qualifications - 5+ years of experience in software engineering, data engineering, or technical consulting — with at least 2+ years on Palantir Foundry
- Strong proficiency in Python (PySpark, Pandas, Polars) and SQL; experience with TypeScript is highly valued
- Deep understanding of the Foundry Ontology — object types, link types, action types, interfaces, and functions
- Proven track record of designing and deploying production-grade data pipelines (batch and streaming) at enterprise scale
- Experience building Workshop applications and/or OSDK-based applications (React/TypeScript)
- Strong communication skills with the ability to engage both technical and non-technical stakeholders
Preferred Qualifications - Experience with Google Cloud Platform (Google Cloud Platform) — Compute Engine, Cloud Functions, Cloud Storage, Dataflow, Pub/Sub, and IAM
- Experience with Google BigQuery — query optimization, partitioning/clustering strategies, BigQuery ML, data warehousing patterns, and migration from legacy EDW platforms (e.g., Netezza, Teradata)
- Experience with AIP (AI Platform) — AIP Logic, AIP Agents, evaluation suites, and LLM-powered functions
- Familiarity with machine learning workflows in Foundry: Model Studio, pro-code model authoring, batch/live inference
- Experience implementing data governance frameworks — markings, ABAC, restricted views, Cipher for data protection, and HIPAA-compliant data handling
- Background in healthcare, kidney care, or clinical operations, revenue cycle optimizations a plus.
- Experience leading teams of 3+ engineers in forward-deployed settings
- Contributions to internal tooling, open-source projects, or platform evangelism
Technical Skills Summary Category | Technologies & Skills | Languages | Python, TypeScript, SQL, Java | Data Engineering | PySpark, Pandas, Polars, DuckDB, Pipeline Builder, Incremental Pipelines | Cloud Platforms | Google Cloud Platform (Google Cloud Platform), BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Functions | Ontology | Object Types, Link Types, Action Types, Interfaces, Functions, OSDK | Applications | Workshop, OSDK (React/TypeScript/Python), Custom Widgets | AI / ML | AIP Logic, AIP Agents, Model Studio, LLM Functions, Evaluation Suites, BigQuery ML | Data Governance | Markings, Permissions, Restricted Views, Cipher, ABAC, HIPAA Compliance | Infrastructure | Data Connections, Connectors, Syncs (Batch/Streaming/CDC), Schedules, Google Cloud Platform Networking |
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