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Software Engineer Data Infrastructure Kafka Jobs

Figma

Software Engineer, Data Infrastructure

$117K - $140K/yr

... Software Engineer for their Data Infrastructure team. This role involves designing and building ... as Spark, Flink, Kafka, or Airflow/Dagster. • Proven track record of impact-driven problem ...

Wing

Software Engineer, Data Infrastructure

Palo Alto, CA · Hybrid

$134K - $161K/yr

About the Role: Wing is looking for a Software Engineer, Data Infrastructure to join our Flight Systems team. This role is Hybrid based in our Palo Alto, California headquarters. The Data ...

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Software Engineer Data Infrastructure Kafka information

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$44.5K

$129.7K

$177.5K

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

As of Aug 29, 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 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 the key skills and qualifications needed to thrive as a software engineer data infrastructure Kafka?

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 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.

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.

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What job categories do people searching Software Engineer Data Infrastructure Kafka jobs look for?

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Software Engineer Data Infrastructure Kafka jobs near you
Infographic showing various Software Engineer Data Infrastructure Kafka job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Software Engineer, Data Infrastructure

Figma

Remote

$117K - $140K/yr

Full-time

Re-posted 21 days ago

Job description

Job Summary:
Figma is a company on a mission to make design accessible to all, and they are seeking a Software Engineer for their Data Infrastructure team. This role involves designing and building large-scale distributed data systems that power analytics and AI/ML across the company, while collaborating with various stakeholders to ensure high-quality data solutions.
Responsibilities:
• Design and build large-scale distributed data systems that power analytics, AI/ML, and business intelligence across Figma.
• Develop batch and streaming solutions to ensure data is reliable, efficient, and scalable across the company.
• Manage and evolve core platforms like Snowflake, our ML Datalake, orchestration infrastructure, and real-time ingestion systems.
• Improve data reliability, consistency, and compliance, ensuring high-quality data for engineering, research, and business stakeholders.
• Identify and drive cost optimization opportunities across data processing, compute infrastructure, and storage.
• Collaborate with AI researchers, data scientists, product engineers, and business teams to understand data needs and build scalable solutions.
• Drive technical decisions and best practices for data ingestion, orchestration, processing, and storage.
• Mentor and support engineers, fostering a culture of learning and technical excellence.
Qualifications:
Required:
• 5+ years of backend or infrastructure engineering experience, including designing and building distributed data infrastructure at scale.
• Strong expertise in batch and streaming data processing technologies such as Spark, Flink, Kafka, or Airflow/Dagster.
• Proven track record of impact-driven problem-solving in fast-paced environments, with a strong focus on high-quality, reliable, and performant systems.
• Excellent technical communication skills, with experience collaborating across both technical and non-technical stakeholders.
• Experience mentoring engineers and fostering a culture of learning and technical excellence.
Preferred:
• Familiarity with our stack, including Golang, Python, SQL, frameworks such as dbt, and technologies like Spark, Kafka, Snowflake, and Dagster.
• Experience building data infrastructure for AI/ML pipelines, including model serving, feature stores, or dataset compliance.
• Experience with reverse ETL, personalization platforms, or real-time event ingestion systems.
• Experience with data governance, access control, and cost optimization strategies for large-scale data platforms.
• The ability to navigate ambiguity, take ownership, and drive projects from inception to execution.
Company:
Figma is a collaborative design tool that enables teams to create, prototype, and test digital products on one platform. Founded in 2012, the company is headquartered in San Francisco, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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