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

Job Summary

Are you passionate about turning data and AI into real-world business impact?

At Insulet, this is an opportunity to be at the forefront of enterprise transformation, partnering directly with business leaders to design, build, and scale AI-powered data solutions that drive smarter decisions, accelerate innovation, and create measurable outcomes.

As a Forward Deployed Data Engineer, you will lead the delivery and adoption of cutting-edge data and AI products across the organization. You'll bridge the gap between complex business challenges and scalable, production-grade solutions, helping teams unlock greater speed, automation, reliability, and performance through modern data platforms and AI-enabled capabilities.

As a key member of the Enterprise AI & Data organization, you'll combine deep technical expertise with product thinking, architectural leadership, and strong business partnership. This role is ideal for a hands-on builder and strategic thinker who thrives in ambiguity, enjoys solving high-impact problems, and can guide initiatives from concept to enterprise-wide adoption.

You will play a pivotal role in transforming ideas into measurable business value, ensuring solutions are embedded into day-to-day operations, supported by trusted data, reusable architecture, strong governance, and modern engineering practices.

If you're energized by the opportunity to shape the future of AI and data at scale, drive visible business impact, and work alongside talented teams solving meaningful challenges, we'd love to connect with you.

 The Forward Deployed Data Engineer will lead the design, delivery, and adoption of AI-enabled data engineering solutions across Insulet. This role will translate complex 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, this role requires deep data engineering expertise, AI-native development practices, product thinking, platform architecture, business partnership, and delivery leadership.

The ideal candidate will be a hands-on technical leader who can operate at both strategic and engineering levels. This leader will be accountable for moving data and AI use cases from ambiguous business need to measurable impact, ensuring solutions are embedded into operational workflows and supported by trusted data, reusable architecture, strong controls, and modern platforms.

Key Responsibilities

1. Forward Deployed Engineering and Business Partnership

• •Collaborate with priority business, product, analytics, and AI teams to understand decisions, workflows, constraints, and value opportunities at the point of use.
• •Translate ambiguous business challenges into data product charters, architecture hypotheses, rapid experiments, MVPs, and scalable delivery plans.
• •Build thin vertical slices that connect source data to trusted decisions, then harden successful prototypes into secure, supportable, and reusable enterprise products.
• •Partner with senior business and technology leaders to define outcomes, adoption measures, value cases, roadmaps, and delivery priorities.
• •Act as the technical bridge across data owners, model builders, application engineers, platform teams, Security, Privacy, Quality, and Compliance to remove delivery friction.

2. AI-Native Data Product Development and Integration

• •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 requirements discovery, data profiling, code generation, testing, documentation, lineage, migration, incident triage, and performance optimization.
• •Create governed agentic workflows that can discover data, propose transformations, validate quality, monitor pipelines, and recommend remediation with explicit human review and approval controls.
• •Engineer context layers that combine structured data, documents, metadata, semantic models, vector representations, and business rules so AI systems can reason from trusted enterprise knowledge.
• •Lead multidisciplinary teams through the full data product lifecycle from discovery and architecture to production deployment, adoption, observability, and continuous improvement.
• •Ensure data products and AI-enabled services are actionable and embedded into business applications, analytics experiences, operational processes, and decision workflows.

3. Modern Architecture, Trust, and Engineering Excellence

• •Architect composable data products using 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 suited to the latency, scale, resilience, and decision needs of each use case.
• •Build metadata-driven pipelines, infrastructure as code, automated testing, continuous integration and delivery, environment promotion, and policy-as-code controls.
• •Engineer data quality, lineage, security, privacy, access, retention, auditability, and regulatory requirements into products by design.
• •Implement end-to-end observability for freshness, completeness, schema change, 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 measurable business value.

4. People Leadership and Enterprise Scale

• •Lead, coach, and develop a high-performing team of Data Engineers and cross-functional delivery professionals while remaining close to architecture and implementation.
• •Create reference architectures, starter kits, reusable components, engineering playbooks, and paved-road patterns that enable teams to deliver faster and more consistently.
• •Lead design reviews, code reviews, technical spikes, and post-implementation learning sessions that raise engineering quality and accelerate knowledge transfer.
• •Build communities of practice across data engineering, analytics engineering, AI engineering, architecture, and platform teams.
• •Bring frontline delivery insights into enterprise platform priorities, standards, workforce capabilities, operating models, and investment decisions.

Required Qualifications

• •Bachelor’s or Master’s degree in computer science, engineering, data science, information systems, or a related technical field.
• •10+ years of experience in data engineering, software engineering, platform engineering, or data architecture, including leadership of complex enterprise delivery.
• •Demonstrated 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.
• •Demonstrated ability to lead data products from discovery through MVP, production deployment, adoption, and continuous improvement.

Preferred Qualifications

• •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 dat