Senior Data Engineer, Reliability & Observability (Hybrid - Acton, MA)

Insulet Corporation•Acton, MA

2d•$118,800 - $178,200•Hybrid

About The Position

Join a mission-driven team dedicated to simplifying life for people with diabetes. We’re seeking a Senior Data Engineer, Reliability & Observability to drive meaningful impact through innovative technology, helping our customers enjoy greater freedom and healthier lives while growing your career in a collaborative, forward-thinking environment. 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; DevOps integrates those capabilities into CI/CD; and leadership consumes dashboards and reports to make informed engineering and release decisions. 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.

Requirements

Bachelor’s degree required (preferred field of study in Computer Science, Software Engineering, Data Engineering, or Information Systems); Master’s degree preferred.
7+ years of experience in data engineering, analytics engineering, platform engineering, or related roles; or 5+ years of experience with an advanced degree in a related field.
Strong experience architecting relational database schemas and modeling structured engineering or operational data for scale, maintainability, and long-term reuse.
Deep SQL expertise and hands-on experience with relational databases such as PostgreSQL or equivalent platforms.
Strong experience building and evolving ETL/ELT or other data ingestion and transformation pipelines in production environments.
Experience using Python or another programming language for data processing, automation, or integration tasks.
Strong understanding of observability and telemetry concepts, including metrics, logs, events, and time-series data.
Experience creating scalable reporting and visualization solutions in tools such as Grafana, Power BI, Tableau, or similar platforms.
Demonstrated ability to influence architecture decisions and partner effectively across engineering, infrastructure, and leadership stakeholders.
Strong written and verbal communication skills, including the ability to make complex technical concepts understandable and actionable for a broad audience.
Exceptional verbal and written communication skills, with the ability to influence technical and non-technical stakeholders at multiple levels of the organization.
Proven ability to work independently with minimal supervision while driving complex technical initiatives forward.
Strong technical leadership skills, including the ability to guide, mentor, and advise engineers beyond direct project collaboration.
Demonstrated ability to navigate ambiguity, evaluate tradeoffs, and make sound architectural decisions in evolving technical environments.
Strong time management, multitasking, and prioritization skills.
Proven ability to work collaboratively across cross-functional teams and build alignment without direct authority.
Strong systems thinking and architectural judgment.
Ability to balance short-term delivery with long-term maintainability and scale.
High attention to detail and commitment to data integrity.
Comfort operating in ambiguous spaces and defining structure where one does not yet exist.
Ability to translate unclear or evolving engineering needs into practical, scalable data solutions.
Strong collaboration, advisory, and influence skills across teams without direct authority.
Bias toward simplification, clarity, reuse, and technical durability.
Sound judgment in selecting patterns, tradeoffs, and technologies appropriate for long-term platform evolution.

Nice To Haves

Experience working in test automation, CI/CD, software quality, reliability engineering, or related engineering environments.
Experience with observability tooling such as Grafana, Loki, Prometheus, InfluxDB, or similar technologies.
Experience handling engineering telemetry, event-based data, time-series data, or log-heavy systems at scale.
Experience supporting embedded systems, hardware/software integration environments, or device-adjacent data platforms.
Experience in regulated industries such as medical devices, healthcare technology, or other high-quality engineering environments.
Experience defining standards, governance approaches, retention strategies, lineage, and access control patterns for shared engineering data platforms.
Experience helping organizations move from fragmented or ad hoc reporting toward unified, scalable, and strategically governed data architectures.
Demonstrated history of providing technical leadership, architectural guidance, or mentorship to engineers across teams.

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 DevOps 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.
Support the development of dashboards and reporting experiences for both technical users and leadership stakeholders, including engineering deep dives, program health reporting, and executive-level views.
Drive improvements in data quality, performance, consistency, integrity, and usability across the PSR ecosystem.
Identify architectural bottlenecks and lead remediation strategies across current data storage, schema design, pipeline reliability, and visualization workflows.
Create and maintain documentation, standards, and best practices for schema design, ingestion patterns, data governance, and dashboard enablement.
Mentor and influence other engineers through design reviews, technical recommendations, and practical guidance on scalable data solutions.
Contribute to and help shape a longer-term data and observability strategy that scales with evolving Pod programs, new test types, and future platform needs.