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Seasonal Telemetry Engineer Jobs (NOW HIRING)

Incedo Inc

SRE Architect

Austin, TX · On-site

$56.50 - $75/hr

Strong JSON-based telemetry manipulation and enrichment GenAI & LLM Enablement * Apply GenAI / LLMs ... Explain how you prevent alert storms during high traffic or seasonal spikes. * What makes an alert ...

Leidos

Systems Operations Engineer

Phoenix, AZ · On-site

$87.10K - $157.45K/yr

This position performs daily, weekly, and seasonal operating studies and reliability assessments to ... telemetry validation, and equipment ratings. • Develop real time operational solutions to ...

Leidos

Systems Operations Engineer

Phoenix, AZ

This position performs daily, weekly, and seasonal operating studies and reliability assessments to ... telemetry validation, and equipment ratings. Develop real time operational solutions to mitigate ...

Leidos

Systems Operations Engineer

Phoenix, AZ · On-site

This position performs daily, weekly, and seasonal operating studies and reliability assessments to ... telemetry validation, and equipment ratings. • Develop real time operational solutions to ...

Dollar General

Lead Software Engineer

Goodlettsville, TN · On-site

$148K/yr

... and seasonal items at everyday low prices in convenient neighborhood locations. Learn more about ... pipelines, telemetry/monitoring, and Kubernetes-based deployments • Proficiency with source ...

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Seasonal Telemetry Engineer information

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$14

$41

$74

How much do seasonal telemetry engineer jobs pay per hour?

As of May 30, 2026, the average hourly pay for seasonal telemetry engineer in the United States is $41.85, according to ZipRecruiter salary data. Most workers in this role earn between $31.25 and $53.61 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Seasonal Telemetry Engineer, and why are they important?

To thrive as a Seasonal Telemetry Engineer, you need a solid background in electrical or computer engineering, data analysis, and experience with telemetry systems, often supported by a relevant degree. Familiarity with data acquisition hardware, wireless communication protocols, and software tools like LabVIEW or MATLAB is typically required. Strong problem-solving abilities, attention to detail, and effective teamwork are vital soft skills for this role. These competencies ensure reliable data collection, system performance, and smooth project execution during critical seasonal operations.

What are some common challenges faced by Seasonal Telemetry Engineers, and how can they be addressed?

Seasonal Telemetry Engineers often work in fast-paced environments where timely data collection and equipment reliability are crucial, especially during peak operational periods. Common challenges include troubleshooting remote telemetry systems in unpredictable outdoor conditions, managing tight project timelines, and adapting quickly to new technologies or protocols. Building strong communication with field teams and staying organized with maintenance schedules can help address these challenges. Additionally, proactively familiarizing yourself with the specific telemetry hardware and software used by the organization can greatly improve efficiency and problem-solving abilities.

What are Seasonal Telemetry Engineers?

Seasonal Telemetry Engineers are professionals who are hired on a temporary or seasonal basis to manage, maintain, and troubleshoot telemetry systems. These systems collect and transmit data remotely, often in industries like environmental science, wildlife tracking, agriculture, or utilities. Seasonal Telemetry Engineers might work during peak periods, such as migration seasons or specific project windows, to ensure reliable data collection and system performance. Their responsibilities can include installing sensors, calibrating equipment, and analyzing telemetry data. This role typically requires technical expertise in electronics, data analysis, and problem-solving.

What is the difference between Seasonal Telemetry Engineer vs Data Acquisition Technician?

AspectSeasonal Telemetry EngineerData Acquisition Technician
CredentialsBachelor's in Engineering or related field, certifications in telemetry systemsAssociate's or Bachelor's in technical field, certifications in data collection tools
Work EnvironmentField sites, remote locations, outdoor environmentsLaboratories, field sites, industrial settings
Industry UsageEnergy, environmental monitoring, oil & gasResearch labs, industrial plants, environmental agencies

The Seasonal Telemetry Engineer and Data Acquisition Technician roles both involve working with data collection systems. However, the engineer typically designs, maintains, and troubleshoots telemetry systems in outdoor or remote environments, often requiring engineering credentials. The technician focuses on operating and maintaining data collection equipment, often in controlled settings. Both roles are essential in industries like energy and environmental monitoring, but the engineer's role is more technical and design-oriented, while the technician's role is more operational.

More about Seasonal Telemetry Engineer jobs
What cities are hiring for Seasonal Telemetry Engineer jobs? Cities with the most Seasonal Telemetry Engineer job openings:
What are the most commonly searched types of Telemetry Engineer jobs? The most popular types of Telemetry Engineer jobs are:
What states have the most Seasonal Telemetry Engineer jobs? States with the most job openings for Seasonal Telemetry Engineer jobs include:
What job categories do people searching Seasonal Telemetry Engineer jobs look for? The top searched job categories for Seasonal Telemetry Engineer jobs are:
Seasonal Telemetry Engineer jobs near you
Infographic showing various Seasonal Telemetry Engineer job openings in the United States as of May 2026, with employment types broken down into 8% Full Time, 88% Part Time, and 4% Contract. Highlights an 89% Physical, and 11% Remote job distribution, with an average salary of $87,048 per year, or $41.9 per hour.
SRE Architect

SRE Architect

Incedo Inc

Austin, TX • On-site

$56.50 - $75/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Site Reliability Architect (SRE) Unified Observability & AIOps

Role Summary

We are seeking a Senior SRE with strong expertise in Unified Observability, proactive detection, AIOps, and GenAI-driven operations to support complex, distributed financial services platforms. The role requires hands-on experience designing SLI/SLO-driven monitoring, dynamic thresholds, intelligent alerting, and AI/ML-based anomaly detection across multi-stream architectures.

Key Responsibilities

Observability & Reliability Engineering

  • Design and implement unified observability dashboards across metrics, logs, traces, events, and topology
  • Define and manage SLIs, SLOs, and error budgets aligned to business outcomes
  • Build actionable dashboards for operations, engineering, and leadership
  • Implement alerting strategies using static and dynamic thresholds

Proactive Detection & AIOps

  • Leverage AI/ML/AIOps to detect anomalies, predict incidents, and reduce MTTR
  • Transition monitoring from reactive alerts to proactive insights
  • Implement noise reduction, alert correlation, and root cause analysis
  • Apply baseline modeling, seasonality detection, and anomaly scoring

Distributed Systems & Dependency Analysis

  • Monitor and troubleshoot multi-service architectures involving:
    • Microservices
    • Downstream APIs
    • Kafka / streaming platforms
    • Cloud infrastructure (Terraform, IaC)
  • Identify whether issues originate from:
    • Upstream/downstream dependencies
    • Streaming platform
    • Infrastructure
    • Application code

Tooling & Platforms

  • Deep hands-on experience with Dynatrace (mandatory)
  • Experience with:
    • OpenTelemetry
    • Prometheus / Grafana
    • ELK / EFK
    • Cloud-native monitoring (AWS/Azure/Google Cloud Platform)
  • Strong JSON-based telemetry manipulation and enrichment

GenAI & LLM Enablement

  • Apply GenAI / LLMs for:
    • Incident summarization
    • Root cause explanation
    • Runbook recommendations
    • Auto-remediation suggestions
  • Collaborate with platform teams to operationalize GenAI safely

Required Skills & Experience

15+ years in SRE / Production Engineering
Strong Unified Observability background (not infra-only)
Hands-on Dynatrace experience (metrics, traces, logs, Davis AI)
SLI/SLO engineering experience in production systems
Experience implementing dynamic thresholds and anomaly detection
Knowledge of AI/ML concepts applied to Ops (AIOps)
Distributed systems troubleshooting expertise
Experience with Kafka or streaming data platforms

Differentiators (Highly Valued)

  • Experience in financial services or regulated environments
  • Proven reduction of alert noise and MTTR using AIOps
  • GenAI / LLM integration into operations workflows

Interview Question Bank (Mapped to LPL Expectations)

  1. Dashboards, SLAs, and Reliability Targets

Purpose: Identify true SREs vs dashboard builders

  • How do you design dashboards differently for engineers vs leadership?
  • Explain how SLIs and SLOs differ from SLAs. Which do you operationalize?
  • How do you map SLOs to alerting without creating noise?
  • What KPIs would you track for a critical trading or advisor-facing platform?

Red Flag: Talks only about CPU, memory, uptime

  1. Alerting Strategy & Threshold Design

Purpose: Assess signal-to-noise maturity

  • How do you decide when to use static vs dynamic thresholds?
  • Explain how you prevent alert storms during high traffic or seasonal spikes.
  • What makes an alert actionable?
  • How do you design alerts for early symptom detection?

Follow-up

  • What happens after an alert fires? Walk me through the lifecycle.
  1. Dynamic Thresholds & Anomaly Detection

Purpose: Validate AIOps fundamentals

  • How do dynamic thresholds work under the hood?
  • How do you account for baseline drift and seasonality?
  • What risks do dynamic thresholds introduce?
  • How would you tune sensitivity to avoid false positives?

Expected Concepts Baselines
ML models
Adaptive learning
Time-series analysis

  1. Multiplexing (Metrics, Signals, Streams)

Purpose: Test system observability depth

  • What is multiplexing in observability?
  • How do multiple telemetry signals strengthen diagnosis?
  • Provide an example where one signal was misleading.
  • How do you correlate metrics, traces, logs, and events?
  1. JSON Tooling & Proactive Detection

Purpose: Ensure hands-on operational telemetry skills

  • How have you used JSON-based event payloads to enrich observability?
  • How do you normalize data across heterogeneous sources?
  • How do structured logs improve proactive detection?
  • How do you extract signals from high-volume telemetry?
  1. Proactive vs Reactive Detection

Purpose: Directly aligned to LPL concern

  • Give an example where you predicted an incident before customer impact.
  • What indicators help you identify impending failures?
  • How do you measure the success of proactive detection?
  1. Multi-Service Failure Diagnosis (Critical Question)

Purpose: Core differentiator at LPL

Scenario Question

A user-facing issue is reported. The architecture includes:

  • Frontend
  • Backend microservices
  • Downstream APIs
  • Kafka streams
  • Terraform-managed infrastructure

Ask:

  • How do you determine if the issue is:
    • Application-related?
    • Kafka or streaming lag?
    • Downstream API latency?
    • Infrastructure drift via Terraform?

Expected Approach Dependency mapping
Golden signals
Trace correlation
Change analysis

  1. Dynatrace (Mandatory)

Purpose: Address explicit gap in feedback

  • What Dynatrace features have you used most?
  • How does Davis AI determine root cause?
  • How do you implement service-level baselining in Dynatrace?
  • How do you reduce alert noise using Dynatrace?

Red Flag: I ve mostly used dashboards

  1. AI/ML & AIOps Fundamentals

Purpose: Ensure non-theoretical knowledge

  • What ML techniques are commonly used in AIOps?
  • How do supervised vs unsupervised models differ in Ops?
  • Where does AI fail in observability?
  • How do you validate AI-based decisions?
  1. GenAI & LLM Use Cases for SRE

Purpose: Explicit LPL requirement

  • Where do you see GenAI adding value in SRE?
  • Have you used LLMs for incident response?
  • How would you integrate GenAI without introducing risk?
  • What data would you restrict from LLM exposure?

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