Software Engineer, Big Data
cognitiv • Bellevue, WA
No Relocation
Posted: May 6, 2026
Job Description
The Role
This Software Engineer, Big Data is accountable for building and scaling high-performance data systems by developing robust data pipelines and infrastructure, working closely with Data Science, Machine Learning, and Engineering teams to deliver reliable, large-scale data solutions that power analytics and AI-driven initiatives.
This position will be located in Bellevue with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote optional (Thursday/Friday).
What You’ll Do
- Build and scale core data infrastructure. You will design, build, and scale large-scale data ingestion, processing, and warehousing pipelines that support analytics, ML, and activation use cases.
- Optimize data processing at scale. You will write and optimize complex SQL and Spark queries to efficiently handle high-volume, distributed datasets.
- Evolve the data platform. You will contribute to the development and ongoing evolution of our data platform, including systems such as the identity graph and ML feature pipelines.
- Ensure system reliability and performance. You will monitor, troubleshoot, and improve highly available data systems to ensure reliability across critical workflows.
- Partner across technical teams. You will collaborate cross-functionally with Data Science, Machine Learning, and Product teams to enable and support data-driven initiatives.
- Improve scalability and efficiency. You will enhance system performance, reliability, and scalability across billions of events and diverse data sources.
- Work with modern data technologies. You will use and extend tools such as Spark, Kafka, Iceberg, and cloud-based infrastructure to build robust data solutions.
Who you are:
- Experienced engineer. You bring 4+ years of experience working with a managed language such as Java or .NET, building production-grade systems.
- Hands-on big data practitioner. You have at least 1+ years of experience working directly with Spark or similar technologies in production environments.
- Cloud-native engineer. You have experience building and operating systems in cloud environments such as AWS, Azure, or GCP.
- Strong SQL and data optimization skills. You write and optimize SQL queries to support efficient processing of large-scale datasets.
- Distributed systems thinker. You have a solid understanding of distributed systems and how to design for scale, reliability, and performance.
- Comfortable with large-scale data debugging. You confidently work with large datasets and troubleshoot complex data issues across pipelines and systems.
- Collaborative engineering partner. You bring a strong communication style and work effectively across engineering, science, and product teams.
- Growth-oriented and highly coachable. You will demonstrate strong learning agility, seek feedback, and continuously improve your technical skills and impact over time.
Bonus Points If You Have:
- Experience scaling large datasets using SQL and Spark
- Background working with high-volume, real-time data systems
- Experience operating and maintaining highly-available systems
- Proficiency in Python
- Familiarity with tools such as Kafka, ClickHouse, or AWS EMR/S3
What Success Looks Like in Your First 30/60/90 Days
What success looks like in your first 30 days:
- Ramp quickly on our data platform, tools, and architecture
- Build strong context on existing pipelines, systems, and key challenges
- Establish relationships with team members and cross-functional partners
- Begin contributing to small improvements or bug fixes
What success looks like in your first 60 days:
- Independently own components of data pipelines or infrastructure
- Deliver measurable improvements in performance, reliability, or scalability
- Align with stakeholders on priorities and technical direction
- Start contributing to design discussions and technical decisions
What success looks like in your first 90 days:
- Fully own key parts of the data platform or pipeline ecosystem
- Deliver measurable business impact through improved data systems
- Drive improvements to at least one core system or process
- Operate autonomously and act as a trusted partner across teams
Salary: $130,000 - $170,000 USD Base Salary + Equity
Additional Content
The Role
This Software Engineer, Big Data is accountable for building and scaling high-performance data systems by developing robust data pipelines and infrastructure, working closely with Data Science, Machine Learning, and Engineering teams to deliver reliable, large-scale data solutions that power analytics and AI-driven initiatives.
This position will be located in Bellevue with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote optional (Thursday/Friday).
What You’ll Do
- Build and scale core data infrastructure. You will design, build, and scale large-scale data ingestion, processing, and warehousing pipelines that support analytics, ML, and activation use cases.
- Optimize data processing at scale. You will write and optimize complex SQL and Spark queries to efficiently handle high-volume, distributed datasets.
- Evolve the data platform. You will contribute to the development and ongoing evolution of our data platform, including systems such as the identity graph and ML feature pipelines.
- Ensure system reliability and performance. You will monitor, troubleshoot, and improve highly available data systems to ensure reliability across critical workflows.
- Partner across technical teams. You will collaborate cross-functionally with Data Science, Machine Learning, and Product teams to enable and support data-driven initiatives.
- Improve scalability and efficiency. You will enhance system performance, reliability, and scalability across billions of events and diverse data sources.
- Work with modern data technologies. You will use and extend tools such as Spark, Kafka, Iceberg, and cloud-based infrastructure to build robust data solutions.
Who you are:
- Experienced engineer. You bring 4+ years of experience working with a managed language such as Java or .NET, building production-grade systems.
- Hands-on big data practitioner. You have at least 1+ years of experience working directly with Spark or similar technologies in production environments.
- Cloud-native engineer. You have experience building and operating systems in cloud environments such as AWS, Azure, or GCP.
- Strong SQL and data optimization skills. You write and optimize SQL queries to support efficient processing of large-scale datasets.
- Distributed systems thinker. You have a solid understanding of distributed systems and how to design for scale, reliability, and performance.
- Comfortable with large-scale data debugging. You confidently work with large datasets and troubleshoot complex data issues across pipelines and systems.
- Collaborative engineering partner. You bring a strong communication style and work effectively across engineering, science, and product teams.
- Growth-oriented and highly coachable. You will demonstrate strong learning agility, seek feedback, and continuously improve your technical skills and impact over time.
Bonus Points If You Have:
- Experience scaling large datasets using SQL and Spark
- Background working with high-volume, real-time data systems
- Experience operating and maintaining highly-available systems
- Proficiency in Python
- Familiarity with tools such as Kafka, ClickHouse, or AWS EMR/S3
What Success Looks Like in Your First 30/60/90 Days
What success looks like in your first 30 days:
- Ramp quickly on our data platform, tools, and architecture
- Build strong context on existing pipelines, systems, and key challenges
- Establish relationships with team members and cross-functional partners
- Begin contributing to small improvements or bug fixes
What success looks like in your first 60 days:
- Independently own components of data pipelines or infrastructure
- Deliver measurable improvements in performance, reliability, or scalability
- Align with stakeholders on priorities and technical direction
- Start contributing to design discussions and technical decisions
What success looks like in your first 90 days:
- Fully own key parts of the data platform or pipeline ecosystem
- Deliver measurable business impact through improved data systems
- Drive improvements to at least one core system or process
- Operate autonomously and act as a trusted partner across teams
Salary: $130,000 - $170,000 USD Base Salary + Equity