Key Responsibilities and Required Skills for Telemetry Application Manager

🎯 Role Definition

The Telemetry Application Manager owns the end-to-end lifecycle of telemetry applications and services — from device/edge data acquisition to cloud ingestion, processing, storage, visualization and analytics. This role blends product management, technical architecture, and program delivery: you will define telemetry roadmaps, prioritize features for high-throughput telemetry pipelines, ensure data quality and observability, and collaborate with firmware, backend, data science and operations teams to turn raw signals into actionable insights. Core accountabilities include protocol selection and implementation (MQTT/CAN/HTTP/UDP), cloud-native ingestion and storage (AWS/GCP/Azure), monitoring and alerting (Prometheus/Grafana/Splunk), telemetry schema governance, and operational readiness for scale and reliability.

📈 Career Progression

Typical Career Path

Entry Point From:

Telemetry Engineer / Telemetry Software Engineer
Embedded Systems Engineer or Firmware Engineer with telemetry focus
Data Engineer / Observability Engineer

Advancement To:

Director of Telemetry / Head of Observability
Senior Product Manager – Data or IoT
Engineering Manager / Head of Platform

Lateral Moves:

IoT Product Manager
Site Reliability Engineering (SRE) Manager
Data Platform Manager

Core Responsibilities

Primary Functions

Own the product vision and roadmap for telemetry applications and services, aligning telemetry priorities with business objectives, product goals, and customer needs.
Define and architect scalable telemetry ingestion pipelines (edge -> gateway -> cloud) that reliably process high-throughput streaming data with low latency and high availability.
Lead design and implementation of telemetry protocols and integrations (MQTT, AMQP, HTTP/REST, TCP/UDP, CAN bus, WebSockets), and define recommended patterns for firmware and edge device teams.
Establish and enforce telemetry data contracts, schemas, and governance (JSON/Protobuf/Avro schemas), including versioning, backward compatibility, and validation rules.
Specify and deliver telemetry storage strategies (time-series DBs, object storage, NoSQL/SQL) optimized for query performance, cost, and retention policies.
Drive telemetry observability: create metrics, logs, distributed tracing, dashboards and SLAs using Prometheus, Grafana, Splunk, ELK, or equivalent tools to monitor pipeline health and application performance.
Lead cross-functional programs to integrate telemetry into product features, ensuring telemetry data enables analytics, alerts, automated actions, and ML model inputs.
Manage end-to-end release planning for telemetry services, including staging, canary deployments, rollback strategies, and production readiness reviews.
Define security and compliance requirements for telemetry (data encryption in transit & at rest, authentication/authorization, GDPR/CCPA considerations, secure OTA), and work with InfoSec to mitigate risks.
Implement data quality processes: schema validation, anomaly detection, schema drift alerts, missing field detection, and reconciliation between source and ingested data.
Collaborate with data engineering and data science to design telemetry data models and pipelines that support historical analysis, real-time analytics, and ML feature engineering.
Drive cost optimization for telemetry platforms: storage lifecycle policies, hot/warm/cold tiers, compression, sampling strategies, and workload right-sizing.
Coordinate incident response for telemetry system outages, lead postmortems, document root causes, and implement measures to prevent recurrence.
Define SLAs/SLOs for telemetry ingestion, processing, and query responses; monitor KPIs and report operational health to stakeholders.
Partner with hardware and firmware teams to validate telemetry at the device level: test harnesses, telemetry simulators, end-to-end test suites and certification criteria.
Mentor, hire and coach engineers and product owners on telemetry best practices, observability, and scalable system design.
Operate as the subject-matter expert for telemetry in cross-functional forums, prioritizing backlog items, and negotiating dependencies and timelines with stakeholders.
Evaluate and select third-party telemetry and observability vendors, manage vendor relationships, and lead procurement and integration activities.
Design and maintain CI/CD pipelines for telemetry microservices and infrastructure as code for telemetry platform components (Terraform, CloudFormation).
Ensure telemetry features are production-ready with automated testing (unit, integration, e2e), performance benchmarking, and chaos testing where appropriate.
Build and maintain telemetry documentation (architecture diagrams, runbooks, onboarding guides, API docs and SDKs) to accelerate internal adoption.
Lead privacy-preserving telemetry initiatives: anonymization, pseudonymization, consent management, and data access controls.
Define and implement telemetry retention, archival and deletion policies in accordance with legal and business requirements.
Advocate telemetry product value to business leaders and customers, provide demos, collect feedback, and iterate on roadmap priorities.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Provide training and onboarding sessions for internal users and teams to increase telemetry literacy and adoption.
Assist in budgeting and resource planning for telemetry infrastructure and tools.

Required Skills & Competencies

Hard Skills (Technical)

Telemetry protocols: deep practical experience with MQTT, AMQP, TCP/UDP, WebSockets, CAN bus, and HTTP/REST telemetry patterns.
Data formats and schema tooling: JSON, Protobuf, Avro, schema registries and contract testing.
Cloud platforms and services: hands-on with AWS (Kinesis, S3, DynamoDB, Timestream), GCP (Pub/Sub, BigQuery), or Azure equivalents.
Stream processing & message systems: Kafka, Kinesis, Flink, Spark Streaming, or similar real-time processing frameworks.
Time-series/datastore expertise: Prometheus, InfluxDB, TimescaleDB, Elasticsearch, Cassandra, or other scalable storage solutions.
Observability & monitoring: Prometheus, Grafana, Splunk, ELK stack, Jaeger/OpenTelemetry tracing.
Programming & scripting: Python, Java, Go, or C++ for telemetry ingestion, SDKs, and backend services.
Containerization & orchestration: Docker, Kubernetes, Helm and production deployment patterns.
DevOps & CI/CD: Git, Jenkins/GitHub Actions/GitLab CI, Terraform, CloudFormation, and infrastructure as code practices.
Networking fundamentals: TCP/IP, SSL/TLS, NAT, QoS, and experience diagnosing network-level telemetry issues.
Security & compliance: encryption, IAM, OAuth, token-based auth, and familiarity with privacy regulations (GDPR, CCPA).
Testing & validation: experience implementing automated testing for data pipelines, load & performance testing, and chaos engineering.
Data modeling and analytics: ETL/ELT concepts, SQL, and exposure to data science/ML feature pipelines.
Vendor integration & SDK design: defining telemetry APIs, client SDKs, and backward-compatible releases.

Soft Skills

Strong cross-functional communication: able to translate technical tradeoffs to non-technical stakeholders and executives.
Product mindset: customer-first orientation and a bias for prioritization based on value and risk.
Leadership and people development: mentoring, hiring, performance management and team-building experience.
Strategic thinking: ability to set multi-quarter telemetry platform goals and roadmaps.
Problem solving: structured troubleshooting and root cause analysis under production pressure.
Stakeholder management: negotiate priorities across product, engineering, operations and compliance.
Time management and prioritization: deliver outcomes in ambiguous, fast-moving environments.
Influencing without authority: drive change across teams and senior leaders.
Detail orientation with systems-level thinking: balance low-level telemetry detail with high-level platform outcomes.
Continuous learning and curiosity about emerging telemetry and observability technologies.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Electrical Engineering, Software Engineering, Data Science, or related field (or equivalent practical experience).

Preferred Education:

Master's degree in Computer Science, Data Science, Systems Engineering, or MBA for product-focused applicants.

Relevant Fields of Study:

Computer Science / Software Engineering
Electrical Engineering / Embedded Systems
Data Science / Analytics
Telecommunications / Network Engineering
Control Systems / Instrumentation

Experience Requirements

Typical Experience Range:

7–12+ years of progressive experience in telemetry, observability, data engineering, embedded systems, or related domains.

Preferred:

8+ years of hands-on experience with telemetry systems and at least 2–4 years leading teams or programs; demonstrated record shipping telemetry products that operate at scale and meet SLAs.