Senior Data Engineer

DNV Group; San Diego (CA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

DNV Group in San Diego, CA is seeking a Senior Data Engineer to design, develop, and maintain data services, APIs, and backend platforms that support AI-ready data foundations for scalable platform capabilities. The role emphasizes Python, SQL, FastAPI, data modeling, and cloud-native tech within an agile environment.

You will collaborate with Product, AI Engineering, and Security teams and operate in a dynamic hybrid schedule, typically three days on-site at the San Diego office or client

Qualifications

• Experience designing and maintaining data services and APIs.
• Backend systems engineering for scalable platforms.
• Experience with cloud-native technologies.

Responsibilities

• Design, develop, test, and maintain APIs and platform capabilities.
• Contribute to solution design, implementation, testing, and production support.
• Translate business requirements into scalable, maintainable software solutions.
• Follow engineering standards for API design, data modeling, and observability.
• Develop reusable services for consistent platform capabilities.
• Collaborate with cross-functional teams to deliver secure, reliable software.
• Support applications, workflows, analytics, and AI-enabled capabilities.

Skills

Data modeling

API development

Cloud platforms

Tools

Python

SQL

FastAPI

Job description

The Senior Data Engineer is responsible for designing, developing, and maintaining data services, APIs, backend systems, and AI-ready data foundations that support scalable platform capabilities across Digital & Transformation.

Working within the Data & AI Engineering section, this role contributes hands‑on engineering expertise across Python, SQL, FastAPI, data modeling, API development, and cloud‑native platform technologies. The role supports both deterministic domain workflows through robust backend services and AI‑enabled capabilities through clean, structured, searchable, and governed data foundations.

Working closely with Product, AI Engineering, Application Engineering, Solution Engineering, Platform Reliability, Architecture, and Security teams, this role translates complex business and domain requirements into reliable, performant, secure, and maintainable software solutions.

The ideal candidate combines strong software engineering fundamentals with practical experience delivering APIs, backend platforms, data services, and cloud‑based solutions within agile product development environments.

This position will be in our San Diego, CA; office, presenting a dynamic hybrid schedule where employees will typically spend three (3) days per week working from either a DNV office or client location/site. Further details regarding role‑specific requirements will be shared during the interview process. Other DNV offices may also be considered.

What You'll Do

Software Engineering & Solution Delivery

• Design, develop, test, and maintain APIs, and platform capabilities that support Product and Engineering objectives.
• Contribute hands‑on engineering expertise across solution design, implementation, code review, testing, documentation, and production support.
• Translate business requirements and domain expertise into scalable, maintainable, and production‑ready software solutions.
• Follow and contribute to engineering standards for API design, data modeling, testing, observability, documentation, and maintainability.
• Develop reusable services and components that enable consistent implementation across platform capabilities.
• Collaborate with cross‑functional teams to deliver secure, high‑quality, and reliable software solutions.
• Support software solutions that enable applications, workflows, analytics, reporting, and AI‑enabled capabilities.

API Development & Platform Integration

• Design and build Python‑based services and FastAPI endpoints that expose platform capabilities and domain‑specific functionality.
• Develop reliable SQL solutions for storing, querying, transforming, validating, and serving business data.
• Collaborate with Application Engineering teams on API contracts, integration patterns, performance considerations, and system interoperability.
• Implement secure, scalable, and maintainable APIs that support internal and customer‑facing applications.
• Contribute to integration solutions connecting applications, data services, AI‑enabled capabilities, and enterprise platforms.
• Support adoption of reusable platform services, shared capabilities, and standardized integration patterns.

AI-Ready Data Foundations

• Prepare, structure, validate, store, retrieve, and govern data used by AI‑enabled workflows, analytics solutions, search capabilities, and customer‑facing applications.
• Contribute to data preparation approaches including metadata enrichment, retrieval optimization, evaluation datasets, and quality feedback mechanisms.
• Partner with AI Engineering teams on retrieval‑augmented workflows, document processing pipelines, evaluation activities, and AI solution development.
• Develop and maintain AI‑ready data assets that are secure, traceable, maintainable, and suitable for downstream consumption.
• Support the implementation of deterministic business logic through APIs, reference data, calculations, and reusable services where traditional software approaches are most appropriate.

DevOps, Reliability & Security

• Develop software solutions that are observable, scalable, secure, performant, and production‑ready.
• Contribute to CI/CD pipelines, automated testing, deployment automation, monitoring, logging, and operational support activities.
• Follow secure software development practices aligned with organizational security requirements and industry standards.
• Implement appropriate controls for data protection, access management, auditability, validation, and operational resilience.
• Collaborate with Platform Reliability and Security teams to ensure services and data assets are governed, documented, and maintainable.
• Support troubleshooting, performance tuning, root cause analysis, and production issue resolution.

Modern Development & AI-Enabled Engineering

• Leverage AI‑assisted development tools to improve coding, testing, documentation, troubleshooting, and engineering productivity.
• Apply modern software development practices that balance speed, quality, maintainability, and security.
• Contribute to the effective and responsible use of AI‑assisted engineering workflows and tools.
• Support continuous improvement initiatives that enhance developer productivity, software quality, and delivery efficiency.
• Share engineering knowledge, reusable patterns, and best practices through collaboration and code reviews.

Collaboration & Knowledge Sharing

• Collaborate with Product, AI Engineering, Application Engineering, Solution Engineering, Architecture, Reliability, and Security teams to deliver integrated platform capabilities.
• Participate in agile development activities including sprint planning, backlog refinement, estimation, reviews, and retrospectives.
• Communicate technical concepts, implementation approaches, and trade‑offs clearly to both technical and non-technical stakeholders.
• Contribute to technical documentation, implementation guidance, and knowledge sharing across engineering teams.
• Support a culture of continuous learning, collaboration, and engineering excellence.