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Overnight Kafka information

What is the difference between Overnight Kafka vs Overnight Data Engineer?

AspectOvernight KafkaOvernight Data Engineer
Required CredentialsKnowledge of Kafka, basic scriptingKafka, SQL, scripting, data modeling
Work EnvironmentNight shift, data streaming tasksNight shift, data pipeline development
Industry UsageData streaming and messagingData pipeline and architecture

Overnight Kafka roles focus on managing Kafka clusters and streaming data during night shifts, often requiring knowledge of Kafka and scripting. Overnight Data Engineers handle broader data pipeline tasks, including designing and maintaining data workflows, with similar shift hours. While both roles work in data environments and may require night shifts, Kafka specialists concentrate on streaming platforms, whereas Data Engineers focus on overall data architecture.

What are the key skills and qualifications needed to thrive as an Overnight Kafka Engineer, and why are they important?

To thrive as an Overnight Kafka Engineer, you need strong experience in distributed systems, Apache Kafka administration, and a background in computer science or a related field. Familiarity with monitoring tools like Prometheus, Kafka Connect, and scripting languages (e.g., Python, Bash) is typically required, along with relevant certifications in cloud platforms or data engineering. Excellent problem-solving abilities, attention to detail, and effective communication are crucial soft skills for handling urgent issues during off-hours. These skills and qualities ensure reliable data streaming, quick incident response, and minimal downtime in high-availability environments.

What are the main challenges faced by an Overnight Kafka Engineer and how can they be addressed?

As an Overnight Kafka Engineer, one of the primary challenges is monitoring and maintaining Kafka clusters during off-hours when issues may not be immediately visible to daytime teams. Rapid incident response, effective communication with on-call teams, and troubleshooting high-throughput data pipelines are common aspects of the role. Proactively setting up robust alerting, thorough documentation, and clear escalation procedures can help address these challenges. Additionally, collaborating closely with daytime engineers during handoffs ensures seamless continuity and minimizes downtime.

What does an Overnight Kafka do?

An Overnight Kafka is typically responsible for monitoring and maintaining Kafka data streaming platforms during overnight shifts. Their duties often include ensuring data pipelines are running smoothly, addressing system errors, and responding to alerts outside of normal business hours. This role is essential for organizations that require 24/7 data processing and uptime. Overnight Kafkas also perform routine maintenance, optimize system performance, and may troubleshoot both hardware and software issues. They work closely with engineering and operations teams to ensure that data flows are reliable and secure.
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Overnight Kafka jobs near you
Infographic showing various Overnight Kafka job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 80% In-person, and 20% Remote job distribution.

Member of Technical Staff, Core Backend

Vapi

San Francisco, CA • On-site

$180K - $265K/yr

Full-time

Medical, Dental, Vision

Re-posted 14 days ago

Job description

Voice AI that resolves, not transfers.
Most phone systems trap callers in menus and scripts. Vapi is the platform for deploying voice agents that know your business and can listen, adapt, and resolve in minutes.
  • The numbers: 1 billion calls. 1 million developers. 10x enterprise ARR growth
  • The customers: Amazon Ring, ServiceTitan, New York Life, Intuit, Kavak, and thousands more, from YC startups to the Fortune 500
  • The news: a $50M Series B led by Peak XV Partners, with Bessemer Venture Partners, Kleiner Perkins, M12 (Microsoft's Venture Fund), Y Combinator, and our earlier backers. Total raised: $72M

Why We're Hiring This Role:
The StreamModule pipeline - VAD → STT → LLM → TTS → Transport - runs on cork/uncork backpressure during live phone calls. A hundred milliseconds of delay is audible. This role owns pipeline stability and pluggability, so the agents and FDE teams can add new models and providers without touching core.
You'll consolidate BullMQ into Kafka, harden the provider abstractions (LLM, STT, TTS base classes), instrument the pipeline with event-driven OTEL tracing, and shore up the Postgres SPOFs that contributed to the Oct 15 and Oct 22 incidents.
What You'll Do:
30 Day: Ramp on the StreamModule pipeline and the cork/uncork backpressure model. Walk the Oct 15 / Oct 22 DB incidents and the duplicate-message incident. Land a scoped pipeline or provider-abstraction improvement.
60 Day: Own a slice of the BullMQ → Kafka consolidation. Ship event-driven OTEL instrumentation for at least one critical pipeline stage. Harden one provider plugin path so a new model can be added without core changes.
90 Day: Drive a measurable reliability or latency win on the call path. Be the backend owner that agents and FDE teams pull in for design reviews on new providers and pipeline changes.
Who You Are:
Must-haves:
  • You've built real-time or streaming systems in production - media pipelines, streaming data, or event-driven backends. You've debugged a backpressure cascade.
  • You have opinions on queue architecture (BullMQ, Kafka, Temporal) and when each is the right fit.
  • You've built plugin or adapter architectures - extending base classes cleanly, with decoupled implementations.
  • You've operated Postgres at scale: connection pooling, read replicas, schema migrations (Liquibase or similar).
  • You instrument with OpenTelemetry and think in event-driven traces, not just logs.

Nice-to-haves:
  • TypeScript + Node.js + NestJS. The codebase is huge NestJS, but a strong systems-thinking engineer ramps fast - language doesn't gate the hire.

Tech stack you'll work in:
  • Languages: TypeScript on Node.js (primary).
  • Framework: NestJS (large codebase).
  • Pipeline: StreamModule (VAD → STT → LLM → TTS → Transport), cork/uncork backpressure.
  • Queues: BullMQ (current), Kafka (target - consolidation on roadmap), Temporal.
  • Database: Postgres (connection pooling, read replicas), Liquibase for schema migrations.
  • Plugin system: provider abstractions - LLM, STT, TTS base classes (pluggable, decoupled from model integrations).
  • Observability: OpenTelemetry tracing, event-driven instrumentation.

Where you likely come from:
A streaming or real-time platform (Discord, Slack, Zoom, Twitch, Mux, LiveKit), an ML-infra company (Modal, Baseten, Replicate, Together), or a pipeline/workflow shop (Temporal, Stripe Radar, trading systems).
Weak fit: backend engineer who's only built systems where users don't wait in real time (overnight jobs, reports, dashboards).
Why Vapi:
Generational impact: Build the human interface for every business
Ownership culture: 70% of the company are previous founders
Kind team: The founders, Jordan and Nikhil, are Canadians
Tier-1 Investors: YC, KP seed, Bessemer Series A, Recent Series B raise
What We Offer:
Real stake: We offer a competitive salary and excellent equity ownership
Comprehensive health coverage: medical, dental, and vision plans
Team love: We love hanging out, and we do quarterly off-sites
Flexible time off: take what you need
More: catered meals, transportation, gym, and a $10k annual L&D budget

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