Data Analytics Engineer

Steel Point Solutions•Washington, DC

3h•$120,000 - $144,000

About The Position

Steel Point Solutions is seeking Data Analytics professionals to join our growing team. The Data Analytics team will support the development and implementation of modern data solutions leveraging Palantir Foundry and related technologies. These resources will work across data engineering, ontology development, and enterprise data architecture to transform complex data into actionable insights, improve operational workflows, and enable data-driven decision-making.

Requirements

Experience in data engineering, data architecture, ontology development, or a related data analytics discipline.
Experience developing and managing data pipelines, integrations, data models, or analytics solutions.
Experience with enterprise data architecture, data governance, or technical solution design.
Ability to translate business and operational requirements into scalable technical data solutions.
Strong analytical, problem-solving, and communication skills.
Ability to work effectively with cross-functional technical and business teams.
DOD Secret Clearance Required

Nice To Haves

Hands-on experience with Palantir Foundry.
Experience developing data pipelines and integrations within Palantir Foundry.
Experience designing and implementing Palantir Ontology, business object models, and operational workflows.
Experience developing enterprise data architectures and technical solution designs leveraging Palantir capabilities.
Experience with data governance frameworks, data modeling, and enterprise information management.
Experience supporting federal government, Department of Defense, or other mission-focused organizations.
Experience working in Agile or other modern software development environments.

Responsibilities

Develop, manage, and optimize data pipelines and data integrations within Palantir Foundry and related environments.
Design and implement scalable analytics solutions to support operational and business requirements.
Integrate data from multiple sources while ensuring data quality, availability, and reliability.
Troubleshoot data pipelines, integrations, and analytics workflows to identify and resolve technical issues.
Design and implement data ontologies and business object models within Palantir Foundry.
Translate business and operational requirements into structured data models and interconnected business objects.
Develop operational workflows that leverage ontology-driven data to support decision-making and business processes.
Collaborate with stakeholders to refine ontology structures and ensure alignment with mission and organizational objectives.
Develop enterprise data architectures, data models, and technical solution designs leveraging Palantir capabilities.
Establish scalable and sustainable approaches for data integration, storage, processing, and analytics.
Develop and maintain data architecture documentation, standards, and technical specifications.
Ensure data solutions align with enterprise architecture, security, governance, and organizational requirements.
Support the development and implementation of data governance frameworks and standards.
Establish practices for data quality, lineage, accessibility, and consistency.
Identify opportunities to improve data management processes and eliminate data silos.
Ensure data solutions adhere to established technical and governance standards.
Collaborate with data scientists, analysts, engineers, architects, business stakeholders, and technical leadership.
Translate business requirements into technical data solutions and communicate complex technical concepts to non-technical stakeholders.
Participate in requirements gathering, solution design, development, testing, and implementation activities.
Provide technical recommendations and contribute to continuous improvement initiatives.
Research and evaluate emerging data technologies, analytics capabilities, and best practices.
Identify opportunities to leverage Palantir Foundry capabilities to improve operational efficiency and decision support.
Recommend enhancements to existing data pipelines, architectures, ontologies, and analytics solutions.
Support the adoption of modern data engineering and architecture practices.