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

Job Summary

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.

Key Responsibilities

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.

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.

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.

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 data.
• Familiarity with privacy, security, validation, responsible AI, auditability, and enterprise governance requirements for data and AI solutions.