Lead Data Engineer – PySpark, Palantir Foundry

Logic20/20, Inc.

. Deliver client value and ensure high client satisfaction .

Posted 10/2/2026full-timeRemote • Washington • United StatesSenior💰 $156,348 - $175,194 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in data engineering and cloud-based data infrastructure, with a strong focus on pipeline design, governance, and reproducibility. Capable of leading technical teams and ensuring high client satisfaction through effective communication and collaboration.

Highest-signal resume keywords

Data EngineeringPython ProgrammingPySpark ExpertiseCloud ServicesRelease Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DesignSQLCloud-Based Data InfrastructureCode RefactoringDataset VersioningAudit ReadinessGeospatial Data PlatformsMachine Learning IntegrationPartitioning StrategiesDocumentation Quality

Soft Skills

Strong Communication SkillsDetail-OrientedGovernance-Minded

Tools & Technologies

GitPalantir FoundryGIS Technologies

Industry Keywords

Regulated IndustriesFinancial ServicesHealthcareUtilitiesInsurance

Tech Stack

Tools & technologies

CloudPySparkPythonSQL

About the role

Key responsibilities & impact

Deliver client value and ensure high client satisfaction
Design, enhance, and maintain production-grade data pipelines supporting model output aggregation and downstream risk analysis
Establish and mature repository governance practices, including branching strategies, pull request standards, merge policies, release tagging, and version control workflows
Own release engineering practices for reproducible, traceable, and auditable production releases
Refactor and improve pipeline code for modularity, maintainability, scalability, and documentation quality
Develop configuration-driven pipeline patterns across environments and releases
Support testing, validation, benchmarking, and change management for critical data pipelines
Partner with data scientists, machine learning engineers, data engineers, product stakeholders, and other technical teams
Align teams on schemas, interfaces, inputs, and delivery expectations
Translate complex technical concepts into clear updates for technical and non-technical stakeholders
Improve structure and governance in codebases, repositories, and engineering workflows
Contribute to engineering best practices in a regulated, audit-sensitive delivery environment

Requirements

What you’ll need

10-15+ years of data engineering, data science, machine learning engineering, and/or relevant experience using Python
Experience leading technical teams and overseeing enterprise-scale data initiatives
Strong expertise in PySpark, SQL, and cloud services
Ability to improve, refactor, or stabilize existing codebases and pipeline environments
Experience with cloud-optimized datasets, efficient partitioning strategies, and large-scale spatial operations
Understanding of machine learning model outputs flowing into downstream data pipelines, platforms, or production systems
Experience in highly regulated industries such as utilities, financial services, healthcare, insurance, or similar environments
Experience designing maintainable, scalable, and well-documented cloud-based data infrastructure or modern data platform environments
Experience supporting reproducibility, dataset versioning, release traceability, and audit readiness
Ability to define expected inputs, outputs, schemas, and interfaces across technical teams
Strong communication skills
Detail-oriented, governance-minded approach to engineering
Practical experience with Git-based workflows, code reviews, branching strategies, and release management
Experience with Palantir Foundry is highly preferred
Experience with GIS technologies and geospatial data platforms

Benefits

Comp & perks

Competitive base salary
Performance-based bonuses
Other incentives
Training and mentorship opportunities
Project opportunities for career development
Supportive, globally connected work environment