Senior Data Engineer

Prescient Edge is seeking a Sr. Data Engineer to support a federal government client.

Benefits:

At Prescient Edge, we believe that acting with integrity and serving our employees is the key to everyone's success. To that end, we provide employees with a best-in-class benefits package that includes:

• A competitive salary with performance bonus opportunities.
• Comprehensive healthcare benefits, including medical, vision, dental, and orthodontia coverage.
• A substantial retirement plan with no vesting schedule. Career development opportunities, including on-the-job training, tuition reimbursement, and networking.
• A positive work environment where employees are respected, supported, and engaged.
• Salary range: $160,000 - $180,000. Salary to be determined by the education, experience, knowledge, skills and the abilities of the applicant internal equity, and alignment with market data.

Overview:

We are seeking a senior technical leader to define and drive the technical vision for integrating multiple independent applications, services, and data initiatives into a cohesive, scalable platform.

This role will serve as the primary technical authority responsible for architectural decisions, integration strategy, and system-level design across frontend, backend, and data science efforts. The successful candidate will work closely with product ownership and engineering teams to establish clear boundaries, responsibilities, and contracts between systems while ensuring solutions are practical, scalable, and aligned with long-term organizational goals.

Description:

• Define and own the overall technical architecture for a platform composed of multiple services, applications, and data products.
• Support teams without micromanaging, enabling autonomy within clear architectural guardrails.
• Make final technical decisions regarding service boundaries and ownership, shared vs. isolated infrastructure (e.g., databases, storage), integration patterns (APIs, eventing, shared libraries, contracts).
• Establish architectural standards, patterns, and guiding principles to ensure consistency across teams.
• Translate products into scalable, maintainable system designs compatible with the security requirements for the deployment environment.
• Produce architectural artifacts such as high-level system diagrams, interface and microservice contract definitions, integration and deployment standards.
• Identify technical risks and tradeoffs early and guide teams toward pragmatic solutions.
• Guide the transition of data science projects from prototypes to production-ready, scalable services.
• Define integration patterns for: Data access, model outputs and UI consumption of data and analytics