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JTEC Consulting•Chantilly, VA

22h•Onsite

About The Position

JTEC Consulting is actively seeking a skilled Mid-Level Palantir Data Engineer to join a high-impact, agile data team responsible for modernizing, transforming, and operationalizing data workflows across enterprise environments. In this role, you will leverage advanced Python and the Palantir Foundry platform to engineer robust data pipelines, integrate diverse external APIs, develop tailored user interfaces, and write custom TypeScript functions. Qualified candidates will have experience beyond core pipeline engineering to support a critical technical bridge between end-users and engineering architecture performing Tier 2 customer triage, translating complex user needs into actionable technical requirements, and building tools to manipulate both structured and unstructured data across varying states and formats. 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)
3+ years of related professional software engineering or data engineering experience
Effective written and verbal communication skills to work as part of a collaborative cross-functional team
Palantir Ecosystem: Proven hands-on proficiency with Palantir Foundry , including deep operational experience with: Palantir Pipeline Builder and Palantir Workshop
Programming Languages: Advanced proficiency in Python (data manipulation, ETL design, API parsing, scripting).
Professional experience writing and integrating TypeScript for front-end interfaces and custom functions.
Data Integration: Demonstrated capability connecting to external APIs, handling authentication, and serializing/parsing complex API payloads into relational or object-based data structures.
Data Handling: Strong experience processing, cleaning, and modeling both structured and unstructured data from edge cases and imperfect states.
Support & Requirements: Demonstrated ability to perform Tier 2 technical triage, assess architectural dependencies, and articulate technical trade-offs to non-technical stakeholders.

Nice To Haves

Experience working directly alongside Data Science teams to format and feature-engineer data for predictive modeling.
Familiarity with software engineering best practices, including Git version control, continuous integration, and automated pipeline testing.
Background working in cross-functional, small-team settings where autonomy, rapid prototyping, and end-to-end ownership are prioritized.
Demonstrated success producing clear data lineage and technical documentation.

Responsibilities

Architect, build, and optimize scalable ETL/ELT pipelines using Palantir Pipeline Builder and advanced Python.
Manage end-to-end movement and ingestion of structured and unstructured data into local and enterprise-level storage repositories using compliant, approved methods.
Develop one-time extraction scripts and automated, recurring ingestion pipelines from heterogeneous data sources.
Transform raw, disparate datasets into clean, well-governed enterprise and standalone data models ready for production analytics.
Build direct integrations with external systems via RESTful APIs, parsing raw responses into clean, typed data objects.
Design, build, and configure mission-focused user interfaces inside Palantir Workshop.
Write, test, and deploy custom application functions using TypeScript to extend interface capabilities and user interactions.
Develop custom tools, reusable code libraries, and micro-services to automate operational data engineering workflows.
Serve as the Tier 2 support escalation point to diagnose, triage, and remediate technical data issues reported by end-users.
Translate customer feedback and operational pain points into detailed functional and technical requirements, clearly articulating downstream impacts on the existing environment.
Collaborate closely with Data Scientists, analysts, and project stakeholders to define data schemas, exploration formats, and analytical transformation rules.
Perform operations and maintenance (O&M), performance tuning, and feature enhancements on existing data pipelines and codebases.
Participate actively in peer code reviews, architectural brainstorming, and solution design sessions.
Create and maintain comprehensive engineering documentation, including ETL field-level mappings, code location references, user guides, and access protocols.