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Software Engineer Data Infrastructure Kafka Jobs

Slack

Staff Software Engineer, Data Infrastructure

Seattle, WA · On-site

$130K - $156K/yr

Slack is looking for a Staff Software Engineer to join the Data Infrastructure team, responsible for building secure and scalable infrastructure powering Slack's data ecosystem and supporting data ...

HeyGen

Software Engineer, Data

Palo Alto, CA

$180K - $220K/yr

... Kafka) that ingest and transform massive multi-modal data--text, audio, and video--to train and run ... Data Lakehouse Infrastructure: Architect and manage data lakehouse solutions (e.g., Snowflake ...

Heygen

Software Engineer, Data

San Francisco, CA · On-site

$180K - $220K/yr

... Kafka) that ingest and transform massive multi-modal data--text, audio, and video--to train and run ... Data Lakehouse Infrastructure: Architect and manage data lakehouse solutions (e.g., Snowflake ...

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Software Engineer Data Infrastructure Kafka information

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How much do software engineer data infrastructure kafka jobs pay per year?

As of Jun 8, 2026, the average yearly pay for software engineer data infrastructure kafka in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What is the difference between Software Engineer Data Infrastructure Kafka vs Data Engineer?

AspectSoftware Engineer Data Infrastructure KafkaData Engineer
Primary FocusDeveloping and maintaining Kafka-based data pipelines and infrastructureDesigning, building, and managing data systems and pipelines across various platforms
Required SkillsKafka, distributed systems, programming (Java, Python), data streamingSQL, ETL, data modeling, cloud platforms, scripting
Work EnvironmentCollaborates with data teams, DevOps, and software developers in tech environmentsWorks with data analysts, data scientists, and business teams in data-driven companies

Both roles involve data infrastructure but differ in scope. Software Engineer Data Infrastructure Kafka specializes in Kafka and streaming data pipelines, while Data Engineers focus on broader data systems and pipelines across multiple platforms. The choice depends on whether the focus is on Kafka-specific infrastructure or comprehensive data system management.

What are the key skills and qualifications needed to thrive as a Software Engineer Data Infrastructure Kafka, and why are they important?

To thrive as a Software Engineer Data Infrastructure Kafka, you need strong programming skills (Java, Scala, or Python), experience with distributed systems, and a solid understanding of data architecture, typically supported by a degree in computer science or a related field. Proficiency in Apache Kafka, stream processing frameworks (e.g., Kafka Streams, Flink), and familiarity with cloud platforms (AWS, GCP, or Azure) are essential, with certifications in cloud technologies or Kafka being advantageous. Excellent problem-solving, collaboration, and communication skills help you work effectively in cross-functional teams and address complex data challenges. These skills and qualities are crucial for building reliable, scalable data pipelines that support business-critical applications.

What does a Software Engineer Data Infrastructure Kafka do?

A Software Engineer Data Infrastructure Kafka specializes in designing, building, and maintaining large-scale data systems that use Apache Kafka for real-time data streaming and processing. This role involves developing robust pipelines, ensuring data reliability and scalability, and supporting the integration of Kafka with other data storage and analytics systems. Engineers in this position also monitor system performance, troubleshoot issues, and implement best practices for security and data management. They work closely with data engineers, application developers, and operations teams to deliver high-quality data solutions.

What are some common challenges faced by Software Engineers working on Data Infrastructure with Kafka, and how can they be addressed?

Software Engineers focusing on Data Infrastructure with Kafka often encounter challenges such as ensuring high availability, managing large-scale data throughput, and maintaining data consistency across distributed systems. Another common hurdle is tuning Kafka for optimal performance under varying workloads. These challenges can be addressed by implementing robust monitoring, practicing careful partitioning and replication strategies, and collaborating closely with DevOps and data engineering teams. Staying updated with Kafka's latest features and best practices also helps in proactively mitigating issues.
More about Software Engineer Data Infrastructure Kafka jobs
What states have the most Software Engineer Data Infrastructure Kafka jobs? States with the most job openings for Software Engineer Data Infrastructure Kafka jobs include:
What job categories do people searching Software Engineer Data Infrastructure Kafka jobs look for? The top searched job categories for Software Engineer Data Infrastructure Kafka jobs are:
Software Engineer Data Infrastructure Kafka jobs near you
Infographic showing various Software Engineer Data Infrastructure Kafka job openings in the United States as of May 2026, with employment types broken down into 11% Internship, and 89% Full Time. Highlights an 89% In-person, and 11% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.
Staff Software Engineer, Data Infrastructure

Staff Software Engineer, Data Infrastructure

Salesforce

Seattle, WA • On-site

$130K - $156K/yr

Full-time

Posted 25 days ago

Salesforce rating

7.8

Company rating: 7.8 out of 10

Based on 48 frontline employees who took The Breakroom Quiz

99th of 186 rated software companies

Job description

Job Summary:
Salesforce is the #1 AI CRM, focusing on customer success through innovation and technology. They are seeking a Staff Software Engineer to join the Data Infrastructure team, responsible for designing and operating scalable data infrastructure that supports analytics and machine learning. The role involves ensuring the reliability and performance of core data services and collaborating with various teams to enhance the data ecosystem.
Responsibilities:
• Design, build, and operate reliable and scalable data infrastructure powering Slack’s analytics, ML, and data-driven decision-making.
• Serve as DRI for multiple core data services specifically on our analytics infrastructure (e.g., StarRocks, Pinot, and Trino), ensuring uptime, reliability, and our compute and orchestration services (e.g., Airflow, Temporal, EMR, Hive Metastore).
• Drive improvements in security, cost efficiency, and developer experience across our data infrastructure.
• Build automation and self-service tools that empower our team and other data teams to easily adopt and manage data workflows.
• Collaborate closely with data engineering, platform, and security teams to design scalable, well-governed solutions.
• Build and enhance our observability, monitoring, and alerting on our services via Grafana and related tooling.
• Partner with other staff and senior engineers to define best practices, technical standards, and support models for Slack’s data ecosystem.
• Mentor and coach other engineers, modeling ownership, collaboration, and operational excellence.
Qualifications:
Required:
• U.S. Citizenship or Permanent Residency (Green Card holder). We are unable to provide visa sponsorship for this role.
• 10+ years of software, platform, or infrastructure engineering experience, including time spent supporting data-intensive systems or data platforms.
• Excellent communication skills and the ability to collaborate across cross-functional teams.
• Proven experience in building, deploying, and operating distributed infrastructure at scale.
• Strong technical background with big data and infrastructure technologies — such as Pinot, StarRocks, Trino, EMR, Airflow, Hive Metastore, Kubernetes, or equivalent systems.
• Proficiency in Python, Golang, Bash, and SQL.
• Proficiency with CI/CD (GitHub Actions), Vault, Terraform, Chef, and Grafana.
• Deep understanding of infrastructure reliability, observability, and cost efficiency principles.
• Hands-on experience supporting data pipelines or data engineering workflows is a strong plus.
• A strong sense of ownership and a drive to deliver high-impact, autonomous results.
Company:
Salesforce is a cloud-based software company that provides customer relationship management software and applications. Founded in 1999, the company is headquartered in San Francisco, USA, with a team of 10001+ employees. The company is currently Late Stage.

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