Performance Monitoring

Performance Monitoring Engineer Jobs

2 jobs found

2mo ago

Senior Data Engineer, Reliability & Observability

Insulet Corporation Acton, MA

Acton, MA

2mo ago Closed

$119k - $178k/yr Full time Senior Analytics Business Analytics

This position is no longer accepting applications (closed Jun 22, 2026).

Job Description

This position shapes the data foundation powering reliability insights for PSR, delivering scalable models, ingestion, and observability across automated tests, lab workflows, and CI-integrated processes.

Responsibilities

Architect and own scalable, maintainable data models for reliability, automation, lab data, and telemetry-generated information.
Lead database schema design to 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 engineering tools.
Define and standardize shared identifiers, metadata strategies, and data contracts enabling reliable correlation across runs, sessions, devices, builds, environments, and programs.
Create complex data solutions in areas with limited or fragmented standards, tooling, or historical patterns.
Provide technical guidance to reliability engineers, lab engineers, software/test automation engineers, and DevOps partners on data architecture, observability design, and scalable reporting patterns.
Influence engineering standards for schema design, ingestion patterns, telemetry structure, data governance, and dashboard consumption.
Enable observability workflows by structuring metrics, logs, events, and related telemetry into fit-for-purpose systems supporting debugging and strategic analysis.
Support dashboards and reporting experiences for technical users and leadership, including engineering deep dives, program health reporting, and executive views.
Drive data quality, performance, consistency, integrity, and usability improvements across the PSR ecosystem.
Identify architectural bottlenecks and lead remediation across 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 and practical guidance on scalable data solutions.
Contribute to longer-term data and observability strategy that scales with evolving Pod programs and future platform needs.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related technical field; Master’s degree preferred.
7+ years of experience in data engineering, analytics engineering, platform engineering, or related roles; or 5+ years with an advanced degree.
Strong experience architecting relational database schemas for scale, maintainability, and long-term reuse.
Deep SQL expertise with hands-on work on PostgreSQL or equivalent relational platforms.
Proven experience building and evolving ETL/ELT or other data ingestion and transformation pipelines in production.
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 Grafana, Power BI, Tableau, or similar tools.
Ability to influence architecture decisions and collaborate with engineering, infrastructure, and leadership stakeholders.
Excellent written and verbal communication skills, capable of explaining complex technical concepts to diverse audiences.
Proven ability to work independently with minimal supervision while driving complex initiatives forward.
Strong technical leadership, mentorship, and advisory capabilities across engineering teams.
Ability to navigate ambiguity, evaluate tradeoffs, and make sound architectural decisions in evolving environments.
Strong time management, multitasking, and prioritization skills.
Proven ability to collaborate across cross-functional teams and build alignment without direct authority.

Technologies

PostgreSQL
SQL
Python
Grafana
Power BI
Tableau
Loki
Prometheus
InfluxDB

Benefits

Medical, dental, and vision insurance
401(k) with company match
Paid time off (PTO)
Additional employee wellness programs

Additional Information

Location: Acton, MA (hybrid) with preference for Acton facility
Travel: approximately 10%, with flexibility based on business needs
Hybrid arrangement eligible (on-site at Acton, MA; partial remote days)
Compensation: base salary range $118,800.00 - $178,200.00 per year
Eligibility for incentive compensation may apply
Application remains open until filled; apply via Insulet Careers
Actual pay depends on skills, experience, and education

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2mo ago

AI/HPC System Performance Engineer

Meta Menlo Park, CA

Menlo Park, CA

2mo ago Closed

$154k - $217k/yr Full time Artificial Intelligence Data Analytics Engineering

This position is no longer accepting applications (closed Jun 16, 2026).

Job Description

The AI and HPC System Performance Engineer will join Meta's Network Infrastructure Engineering team to characterize end-to-end performance, identify bottlenecks, and optimize large-scale AI training and inference clusters. The role sits at the intersection of network fabric design, distributed computing, and AI workload behavior to maximize throughput and efficiency.

Compensation

USD 154,000 - 217,000 per year

Location

Menlo Park, CA (onsite)

Experience

Minimum 6 years of experience in system performance engineering, network infrastructure engineering, or a related field within large-scale distributed computing or HPC environments.

Summary

AI/HPC System Performance Engineer on Meta's Network Infrastructure Engineering team responsible for end-to-end performance characterization, bottleneck analysis, and optimization of large-scale AI training and inference clusters. The role emphasizes the convergence of network fabric design, distributed computing, and AI workload behavior to maximize throughput and efficiency.