Data Analytics Engineer
- Market p25–p75
- $119k–$154k
- Opened · 30d
- +0
- Closed · 30d
- −0
- Opened · 7d
- +1117% of open
- All data engineering
- 3,808open
Steel Point Solutions is an amazing SBA Certified (8a), HUBZone, Small Disadvantaged Business (SDB) and a Woman Owned Small Business (WOSB) company. Established in 2013 with a vision of offering world class, integrated business solutions for all levels of Government and commercial enterprises. We are represented by a team of talented and qualified professionals who know how essential efficient, cost-effective integrated solutions are to your organization’s success. Leveraging these resources, we strive daily to lead the industry in program management and service delivery.
Role Summary
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.
Key Responsibilities
Data Engineering & Analytics Support
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.
Ontology Development & Operational Workflows
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.
Data Architecture & Solution Design
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.
Data Governance & Quality
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.
Cross-Functional Collaboration
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.
Innovation & Continuous Improvement
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.
Required Qualifications
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.