Forward Deployed Data Engineer - Hybrid (Acton, MA or San Diego, CA)

Insulet Corporation•San Diego, CA

2d•$143,700 - $215,550•Hybrid

About The Position

The Forward Deployed Data Engineer leads the design, delivery, and adoption of AI-enabled data engineering solutions across Insulet. The role translates complex business opportunities into scalable, production-grade data products that improve decision quality, speed, automation, reliability, and enterprise performance. As part of the Enterprise AI and Data organization, the position combines data engineering expertise, AI-native development practices, product thinking, platform architecture, business partnership, and delivery leadership. The role is suited to a hands-on technical leader who can work at both strategic and engineering levels, guide data and AI use cases from ambiguous business needs to measurable impact, and ensure products are embedded in operational workflows. Solutions should be supported by trusted data, reusable architecture, strong controls, and modern platforms. The position is hybrid and based in Acton, Massachusetts, or San Diego, California.

Requirements

Bachelor’s or Master’s degree in computer science, engineering, data science, information systems, or a related technical field.
10 or more years of experience in data engineering, software engineering, platform engineering, or data architecture, including leadership of complex enterprise delivery.
Ability to design, code, test, deploy, and operate cloud-scale data products using modern engineering practices.
Strong experience with distributed data processing, data modeling, orchestration, APIs, event streaming, DevOps, DataOps, observability, and infrastructure as code.
Practical understanding of generative AI, large language models, retrieval-augmented generation, vector search, AI agents, model integration, and AI-assisted software development.
Ability to lead data products from discovery through MVP, production deployment, adoption, and continuous improvement.

Nice To Haves

Experience in healthcare, medical devices, life sciences, diabetes care, digital health, or regulated industries.
Experience with platforms such as AWS, Azure, Databricks, Snowflake, Kafka, dbt, Spark, Kubernetes, Terraform, GitHub, or similar enterprise technology ecosystems.
Experience establishing data product operating models, data contracts, semantic layers, real-time architectures, or self-service engineering platforms.
Experience modernizing legacy data estates and integrating ERP, CRM, manufacturing, supply chain, quality, commercial, clinical, or connected-device data.
Familiarity with privacy, security, validation, responsible AI, auditability, and enterprise governance requirements for data and AI solutions.

Responsibilities

Collaborate with business, product, analytics, and AI teams to understand workflows, constraints, decisions, and value opportunities.
Translate ambiguous business challenges into data product charters, architecture hypotheses, experiments, MVPs, and scalable delivery plans.
Build vertical slices connecting source data to trusted decisions, and develop successful prototypes into secure, supportable, reusable enterprise products.
Partner with senior business and technology leaders to define outcomes, adoption measures, value cases, roadmaps, and delivery priorities.
Coordinate across data owners, model builders, application engineers, platform teams, Security, Privacy, Quality, and Compliance to remove delivery friction.
Design and deliver AI-ready data foundations for predictive models, generative AI, retrieval-augmented generation, knowledge systems, copilots, and autonomous or semi-autonomous agents.
Use AI coding copilots and engineering agents to accelerate discovery, data profiling, code generation, testing, documentation, lineage, migration, incident triage, and performance optimization.
Create governed agentic workflows for data discovery, transformation proposals, quality validation, pipeline monitoring, and remediation recommendations, with human review and approval controls.
Engineer context layers combining structured data, documents, metadata, semantic models, vector representations, and business rules for AI systems.
Lead multidisciplinary teams through the data product lifecycle, from discovery and architecture through production deployment, adoption, observability, and continuous improvement.
Embed actionable data products and AI-enabled services into business applications, analytics experiences, operational processes, and decision workflows.
Architect composable data products with domain-oriented ownership, clear interfaces, reusable services, data contracts, semantic standards, and lifecycle accountability.
Design batch, streaming, event-driven, API-based, and change-data-capture patterns based on use-case latency, scale, resilience, and decision needs.
Build metadata-driven pipelines, infrastructure as code, automated testing, CI/CD, environment promotion, and policy-as-code controls.
Build data quality, lineage, security, privacy, access, retention, auditability, and regulatory requirements into products.
Implement end-to-end observability for freshness, completeness, schema changes, data drift, pipeline health, service levels, user impact, and cost.
Apply cloud and AI FinOps practices to optimize compute, storage, data movement, model consumption, and engineering effort against business value.
Lead, coach, and develop Data Engineers and cross-functional delivery professionals while remaining involved in architecture and implementation.
Create reference architectures, starter kits, reusable components, engineering playbooks, and paved-road patterns.
Lead design reviews, code reviews, technical spikes, and post-implementation learning sessions.
Build communities of practice across data engineering, analytics engineering, AI engineering, architecture, and platform teams.
Bring delivery insights into enterprise platform priorities, standards, workforce capabilities, operating models, and investment decisions.