Senior Data Engineer, Reliability & Observability; Hybrid - Acton, MA

Position: Senior Data Engineer, Reliability & Observability (Hybrid - Acton, MA)
Job Title:

Senior Data Engineer, Reliability & Observability

FLSA Status:
Exempt

Company Overview

Insulet started in 2000 driven to achieve our mission of enabling our customers to enjoy simplicity, freedom and healthier lives through the use of our Omnipod product platform. In the last two decades we have improved the lives of hundreds of thousands of patients who have insulin-requiring diabetes by using innovative technology that is wearable, waterproof, and lifestyle accommodating. We are on an exciting trajectory of significant growth and global expansion enabling us to reach more patients around the globe.

We are looking for highly motivated, performance driven individuals who want to be part of building our Center of Excellence and be at the forefront of our rapidly growing global footprint. We are looking to hire amazing people who are guided by shared values and desire to exceed customer expectations. Our continued success depends on it.

Team Overview

The Pod Software Reliability (PSR) team is focused on improving the reliability, robustness, and observability of embedded software systems through scalable automation, data-driven insights, and close cross-functional collaboration. PSR defines reliability scenarios and metrics; automation and lab functions execute large-scale testing and aggregate data;
Dev Ops integrates those capabilities into CI/CD; and leadership consumes dashboards and reports to make informed engineering and release decisions.

Role Summary

We are seeking a highly skilled Senior Data Engineer, Reliability & Observability to architect, build, and evolve the data foundation that powers PSR's reliability insights. This role will lead the design of scalable data models, resilient ingestion patterns, schema strategy, and observability architecture across automated testing, lab execution, and CI-integrated reliability workflows.

This individual will operate with a high degree of independence and technical judgment, shaping how reliability and automation data is structured, governed, and consumed across engineering and leadership stakeholders. The role requires the ability to design solutions in areas where standards may be immature, fragmented, or still evolving, and to translate ambiguous reliability needs into durable, scalable data architectures.

The Senior Data Engineer will serve as a technical advisor across PSR initiatives, influencing architecture decisions, guiding best practices, and helping other engineers adopt scalable approaches to data modeling, telemetry, and observability. This role is intended for someone with deep prior experience in data engineering and observability systems who can reduce operational fragility, improve long-term maintainability, and accelerate self-service visibility across the organization.

Key Responsibilities

* Architect and own scalable, maintainable, and extensible data models for reliability, automation, lab, and telemetry-generated data.

* Lead the design and evolution of database schemas that support cross-test analytics, traceability, repeatability, and long-term platform growth.

* Design and implement robust ingestion and transformation pipelines across automated test systems, CI/CD workflows, lab infrastructure, and supporting engineering tools.

* Define and standardize shared identifiers, metadata strategies, and data contracts that enable reliable correlation across runs, sessions, devices, builds, environments, and programs.

* Design complex or novel data solutions in areas where standards, tooling, or historical patterns are limited, fragmented, or outdated.

* Provide technical guidance and advisory support to reliability engineers, lab engineers, software/test automation engineers, and Dev Ops partners on data architecture, observability design, and scalable reporting patterns.

* Influence and help establish engineering standards for schema design, ingestion patterns, telemetry structure, data governance, and dashboard consumption.

* Enable observability workflows by structuring and integrating metrics, logs, events, and related telemetry into fit-for-purpose systems that support both operational debugging and strategic analysis.

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