Staff Software Engineer, Confluent
Observability
Data streaming platform
Be an early applicant
Data streaming platform
1001+ employees
Be an early applicant
1001+ employees
Company mission
Founded by the team that built Apache Kafka, their mission is to make managing Kafka easy.
Role
Who you are
- 10+ years industry experience designing, building, scaling and supporting backend systems in production with a solid grasp on good software engineering practices such as code reviews, deep focus on quality, and documentation
- Strong programming and algorithmic skills. Proficiency in a major programming language, e.g. Java, Go, C / C++, Python, etc
- Experience configuring and deploying distributed systems and microservices using modern tools, e.g. Kubernetes, Helm, etc
- Hands-on technical expertise in large scale systems engineering or distributed systems
- Strong focus on project delivery and communication skills
- Experience in driving operational excellence for large, production services
- A strong sense of customer centricity, teamwork, technical leadership and mentorship and are excited about team and company success
- BS, MS, or PhD in computer science or a related field, or equivalent work experience
Desirable
- Proven track record of delivering large-scale, highly available, low latency, high quality systems
- On-call experience handling critical systems
- Experience within observability concepts, products, and components, e.g. metrics, traces, logging, Datadog, New Relic, Grafana, OpenTelemetry
What the job involves
- The team’s charter includes building the next-generation Telemetry Platform to level up the reliability, performance, and scale of Confluent’s observability features
- The Telemetry Platform enables collection of metrics and traces from services in our data and control planes to provide insights for service operators and customers of Confluent Cloud
- As a staff software engineer, you will collaborate closely with the team and essential stakeholders to design, architect, and develop our secure multi-tenant Cloud Platform and raise the bar for observability features
- You will have an opportunity to solve complex distributed systems problems at scale
- You will build services that can operate across different cloud service providers like AWS, GCP, and Azure
- Independently drive execution of complex technical projects end to end
- Build mission-critical backend services that deliver value to our customers. You will play a crucial role in architecting, designing, developing and operationalizing high performance, scalable, reliable and resilient services
- Troubleshoot and debug technical issues inside a deep and complex technical stack that includes microservices, containers, and virtualization
- Ensure operational readiness of the services and meet the availability and performance SLA commitments to our customers
Our take
Confluent is a commercial company built on top of the open-source Apache Kafka project. Confluent and Kafka have developed a streaming data technology that processes massive amounts of information in real-time.
The streaming database technology was developed at LinkedIn as a way of moving massive amounts of messages. The company decided to open-source that tech in 2011, and Confluent launched as the commercial arm in 2014.
While the base open-source component remains available for free download, it doesn’t include the additional tooling the company has built to make it easier for enterprises to use Kafka. Confluent's value add is in those additional tools, including a cloud version of the product and a marketplace for sharing extensions to the platform.
Confluent expanded strategic partnerships with Microsoft and acquired Immerok, and in 2023 expanded its cloud platform. 2024 saw further product expansions with the launch of the Confluent Migration Accelerator, all exemplifying how the company is living up to its name by rocket-boosting the benefits of Apache Kafka integration for companies.
Steph
Company Specialist
Insights
Some candidates hear
back within 2 weeks
27% female employees
14% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Apr 2020
$250m
SERIES E
Jan 2019
$125m
SERIES D
Total funding: $455.9m
Company values
- Earn Our Customers' Love - Our customers are the lifeblood of our business and our most important stakeholder. We strive to earn their love with everything we do
- To Build a Great Company, Build a Great Team - We believe that an important part of building a great company is making sure that our employees are the best they can be.
- Smart, Humble, and Empathetic - Our employees appreciate, benefit, and learn from working with other smart, humble, and empathetic people. We strive to hire those who care deeply about others and interact with empathy towards our customers, partners, and employees.
- Be Fired up and Get It Done - As much as we benefit from working with smart, humble, and empathetic employees, we also need to “get it done” to pursue our enormous market opportunity. We are in hyper-growth mode and are scaling to fill the demands of the market and our current and future customers.
- Tasteful not Wasteful - We want to invest in areas that matter the most -- in other words, spend “tastefully,” not wastefully. We believe that thoughtful spending is essential to building a high-quality business.
- One Team - It is critical that we act as and make decisions as a unified company, doing our best to optimize globally rather than locally. This means doing our best to avoid the misalignments that some companies can be prone to. It means recognizing that when employees and teams succeed, Confluent as a whole succeeds.
Company HQ
Mountain View, California
Articles
Leadership
Jay Kreps
(Co-Founder & CEO)Previously a Principal Engineer at LinkedIn.
Neha Narkhede
(Co-Founder & Board Member)Previously a Lead Engineer at LinkedIn and Oracle. They are an Investor/advisor for Gem, Block Party, Material Security, Abacus AI, Cortex Data, Yugabyte, Metaphor Data, Natalist, Common Room, and more.
Jun Rao
(Co-Founder)Previously a Senior Engineer at LinkedIn. Has a PhD in Computer Science from Columbia University.
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