Senior/Staff Software Engineer - Data Platform

Harness is a high-growth company that is disrupting the software delivery market. Our mission is to enable the 30 million software developers in the world to deliver code to their users reliably, efficiently, securely and quickly, increasing customers’ pace of innovation while improving the developer experience. We offer solutions for every step of the software delivery lifecycle to build, test, secure, deploy and manage reliability, feature flags and cloud costs. The Harness Software Delivery Platform includes modules for CI, CD, Cloud Cost Management, Feature Flags, Service Reliability Management, Security Testing Orchestration, Chaos Engineering, Software Engineering Insights and continues to expand at an incredibly fast pace.

Harness is led by technologist and entrepreneur Jyoti Bansal, who founded AppDynamics and sold it to Cisco for $3.7B. We’re backed with $425M in venture financing from top-tier VC and strategic firms, including J.P. Morgan, Capital One Ventures, Citi Ventures, ServiceNow, Splunk Ventures, Norwest Venture Partners, Adage Capital Partners, Balyasny Asset Management, Gaingels, Harmonic Growth Partners, Menlo Ventures, IVP, Unusual Ventures, GV (formerly Google Ventures), Alkeon Capital, Battery Ventures, Sorenson Capital, Thomvest Ventures and Silicon Valley Bank.

Position Summary

We are seeking a talented and experienced Data Platform Engineer to join our team as the Technical Lead. The ideal candidate will be responsible for designing, developing, and maintaining our data pipelines and infrastructure. They will work closely with cross-functional teams to understand data requirements, implement solutions, and ensure data quality and reliability. The data engineer will also be involved in optimizing data processes and supporting data analytics initiatives.

About the role

• Design, build, and maintain scalable data pipelines and infrastructure to support data ingestion, processing, and storage.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and develop solutions to meet business needs.
• Implement data quality checks and ensure data integrity and reliability throughout the data lifecycle.
• Optimize and tune data pipelines for performance and efficiency.
• Troubleshoot and debug data-related issues, and provide timely resolution.
• Stay updated with the latest technologies and best practices in data engineering and apply them to improve existing systems.
• Document data architecture, processes, and workflows.
• Provide support and guidance to junior members of the team.

About you

• Bachelor's degree in Computer Science, Engineering, or related field. Master's degree preferred.
• 3+ yrs of proven experience as a data engineer or similar role, with a strong background in data warehousing, ETL/ELT processes, and data modeling.
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with big data technologies such as BigQuery, Spark, Kafka, DBT, SQLMesh etc.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong SQL skills and experience working with relational and NoSQL databases.
• Familiarity with data visualization tools such as Tableau, Power BI, or Looker.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.

Preferred Experience

• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Knowledge of machine learning techniques and frameworks.
• Experience with DevOps practices and tools for CI/CD.

Work Location

Mountain View, CA - Hybrid (3 days / week onsite) 

What you will have at Harness

• Flexible Spending Account (FSA)
• Employee Assistance Program (EAP)
• Flexible Time Off and Parental Leave
• Monthly, quarterly, and annual social and team building events

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex or national origin.