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JTEC Consulting•Chantilly, VA

20h•Onsite

About The Position

JTEC Consulting is actively seeking a Palantir Platform Engineer to play a pivotal role in integrating custom ETL workflows directly into the Palantir Foundry ecosystem. The Platform Engineer will design, build, and optimize high-throughput data pipelines using Python and PySpark, leverage artificial intelligence to automate and streamline data transformations across massive datasets, and enforce rigorous governance and validation standards to deliver analytics-ready data products. Qualified candidates will demonstrate experience in bridging modern data engineering with enterprise ontology, connecting custom ETL conditioners to Palantir tooling to dramatically accelerate data conditioning, and providing unified, end-to-end operational observability. A current security clearance with agency-specific polygraph is strictly required for consideration.

Requirements

U.S. citizenship
Current TS/SCI with agency-specific polygraph required for consideration
Bachelor’s degree in Computer Science, Information Systems, or a related technical discipline (equivalent years of direct experience considered in lieu of a degree)
Mid-Level: 5+ years of data or platform engineering experience with a focus on Palantir Foundry, PySpark/Python ETL development, and data ontology modeling
Senior-Level: 8+ years of data engineering or platform architecture experience with advanced architectural design of scalable, distributed pipelines, enterprise ontology systems, and end-to-end platform observability frameworks
Effective written and verbal communication skills to work as part of a collaborative, cross-functional team
Palantir Foundry Expertise: Proven hands-on experience building, maintaining, and deploying solutions inside the Palantir Foundry ecosystem (Data Lineage, Code Workspaces, Pipeline Builder, Workshop)
Data Ontology: Deep understanding of enterprise data modeling, semantic layers, and ontology architecture
ETL/ELT Engineering: Proven track record of architecting and integrating complex, distributed data pipelines across heterogeneous sources
Programming and Big Data: Advanced proficiency in Python and distributed computing frameworks (specifically PySpark and Apache Spark)
Logging and Telemetry: Experience implementing comprehensive monitoring, metrics collection, and alerting solutions across data pipelines
Data Governance and Compliance: In-depth knowledge of data governance frameworks, metadata management, RBAC, and data quality controls
DevOps Practices: Familiarity with modern infrastructure-as-code, version control (Git), containerization, and automated CI/CD deployment pipelines
AI-Driven Engineering: Practical exposure to leveraging AI/ML tools and techniques to enhance data engineering productivity and pipeline performance
Data Validation: Strong background in writing automated validation suites, data contract enforcement, and pipeline testing frameworks

Nice To Haves

Direct experience designing and connecting custom ETL conditioners directly to Palantir tooling to standardize and sanitize raw inputs
Demonstrated success in elevating platform observability, reducing MTTR (Mean Time to Resolution) for pipeline failures, and accelerating conditioning cycles
Experience building end-to-end transformation observability dashboards that serve both technical engineers and non-technical business stakeholders
Proven background deploying validated ontology products directly into operational workflows, decision-making applications, or AI/ML operational tooling
Palantir Foundry certifications or recognized training credentials

Responsibilities

Architect, deploy, and maintain scalable ETL/ELT pipelines using PySpark and Python, integrating external data workflows and conditioning processes into Palantir Foundry
Design and configure robust data ontologies within Palantir, mapping complex enterprise data relationships to operational digital twins and analytical models
Connect custom ETL conditioners with native Palantir tooling, implementing telemetry, logging, and monitoring frameworks that give stakeholders real-time visibility across every stage of the transformation lifecycle
Identify and integrate AI/ML capabilities to augment data pipelines, automating anomaly detection, schema mapping, and pipeline optimization to maximize throughput across massive datasets
Establish rigorous validation checks, data lineage tracking, and governance frameworks to verify data accuracy, reliability, and security before deployment to analytics consumers
Prepare and expose validated, high-integrity data products for direct ingestion into advanced analytics, business intelligence, and operational applications
Apply modern CI/CD and DevOps methodologies to manage release lifecycles, configuration management, and platform stability across production environments
Partner with data scientists, software engineers, security teams, and operational stakeholders to translate complex business logic into dependable data architecture