Credit Karma
Staff Software Engineer, Experimentation Platform
Credit Karma, Charlotte, North Carolina, United States, 28245
About the Role
We are seeking a highly motivated and experienced
Staff Data Engineer
to lead the data architecture and engineering strategy for our experimentation platform. In this role, you will build and scale the data systems that enable reliable, consistent, and timely experimentation insights across the company. You will partner with data scientists, analysts, product managers, and other engineering teams to design robust data models, pipelines, and governance practices that support thousands of metrics and diverse statistical methodologies.
This is a high-impact, high-visibility position that
demands strong software engineering and data engineering expertise , architectural leadership, and a passion for building highly available and performant distributed systems. You will define the technical roadmap and strategy for the experimentation platform’s analytics infrastructure, ensuring alignment with business objectives while pushing the boundaries of what’s possible in experimentation and data analysis.
Our experimentation platform is a distributed, highly available, low latency Finagle service. The platform leverages Google Cloud technologies, including Cloud Composer for workflow orchestration, Dataflow for data processing, and BigQuery for data warehousing. Our statistical engine applies a combination of Python libraries (e.g., Statsmodels, SciPy) and custom algorithms to analyze experiment results.
Responsibilities
Define Technical Strategy
Provide the roadmap and architecture for the experimentation platform’s infrastructure, ensuring alignment with business objectives and adherence to industry best practices.
Develop Near Real-Time Systems
Lead critical initiatives to build our next-generation near real-time ecosystem, to enhance near real-time observability and alerting, leveraging Scala, Pub/Sub, Akka, and Dataflow on Google Cloud.
Build Scalable Pipelines
Architect and maintain large-scale batch data pipelines using Google Dataflow, BigQuery, and Airflow/Cloud Composer to handle high-volume, batch data processing.
Develop Core Capabilities
Enhance the experimentation platform with new capabilities such as experiment targeting and localized assignments at scale to reduce latency and improve developer experience.
Optimize Data Infrastructure
Drive efficiency and performance improvements across experimentation pipelines, frameworks, and query layers. Evaluate trade-offs in system design, balancing speed, scalability, cost, and accuracy.
Stay Current with Industry Trends
Research, evaluate, and integrate the latest advancements in experimentation methods, data analysis techniques, and cloud-based technologies to continually improve the platform.
Mentor and Guide
Provide technical leadership and support to junior engineers, fostering a culture of continuous learning and professional growth.
Collaborate on Experiment Analysis
Partner with marketers, analysts, and data scientists to build infrastructure that supports thousands of metrics and various statistical methods (e.g., t-tests, sequential testing, Bayesian analysis).
Qualifications
Bachelor’s or Master’s degree in Computer Science, Statistics, or a related field.
10+ years in software engineering, with a focus on data engineering and data architecture.
Proficiency in Scala, Python, and SQL.
Demonstrated success building and maintaining large-scale data pipelines using technologies such as Spark, Flink, Google Dataflow, BigQuery, or Airflow/Composer.
Familiarity with Python libraries for statistical analysis (e.g., Statsmodels, SciPy).
Deep understanding of software development lifecycle best practices, including agile methodologies.
Excellent communication, collaboration, and stakeholder management skills.
Proven ability to lead complex projects and mentor engineering teams.
Proven expertise in A/B testing methodologies and statistical concepts.
What we would like to see
Previous experience building analytics pipelines at experimentation platform at large-scale tech companies serving millions of users or vendors (e.g., Optimizely, Statsig, LaunchDarkly)
Knowledge of heterogeneous treatment effects and advanced statistical modeling techniques for experimentation.
Experience with adaptive experimentation or Bayesian optimization methods.
Benefits
Medical and Dental Coverage
Retirement Plan
Commuter Benefits
Wellness perks
Paid Time Off (Vacation, Sick, Baby Bonding, Cultural Observance, & More)
Education Perks
Paid Gift Week in December
Equal Employment Opportunity Credit Karma is proud to be an Equal Employment Opportunity Employer. We welcome all candidates without regard to race, color, religion, age, marital status, sex (including pregnancy, childbirth, or related medical condition), sexual orientation, gender identity or gender expression, national origin, veteran or military status, disability (physical or mental), genetic information or other protected characteristic. We prohibit discrimination of any kind and operate in compliance with applicable fair chance laws.
Credit Karma is also committed to a diverse and inclusive work environment because it is the right thing to do. We believe that such an environment advances long-term professional growth, creates a robust business, and supports our mission of championing financial progress for everyone. We offer generous benefits and perks with a single eye to nourishing an inclusive environment that recognizes the contributions of all and fosters diversity by supporting our internal Employee Resource Groups. We’ve worked hard to build an intensely collaborative and creative environment, a diverse and inclusive employee culture, and the opportunity for professional growth. As part of the Credit Karma team, your voice will be heard, your contributions will matter, and your unique background and experiences will be celebrated.
Privacy Policies:
Credit Karma is strongly committed to protecting personal data. Please take a look below to review our privacy policies:
GDPR Privacy Policy
U.S. Job Applicant Privacy Notice
#J-18808-Ljbffr
Staff Data Engineer
to lead the data architecture and engineering strategy for our experimentation platform. In this role, you will build and scale the data systems that enable reliable, consistent, and timely experimentation insights across the company. You will partner with data scientists, analysts, product managers, and other engineering teams to design robust data models, pipelines, and governance practices that support thousands of metrics and diverse statistical methodologies.
This is a high-impact, high-visibility position that
demands strong software engineering and data engineering expertise , architectural leadership, and a passion for building highly available and performant distributed systems. You will define the technical roadmap and strategy for the experimentation platform’s analytics infrastructure, ensuring alignment with business objectives while pushing the boundaries of what’s possible in experimentation and data analysis.
Our experimentation platform is a distributed, highly available, low latency Finagle service. The platform leverages Google Cloud technologies, including Cloud Composer for workflow orchestration, Dataflow for data processing, and BigQuery for data warehousing. Our statistical engine applies a combination of Python libraries (e.g., Statsmodels, SciPy) and custom algorithms to analyze experiment results.
Responsibilities
Define Technical Strategy
Provide the roadmap and architecture for the experimentation platform’s infrastructure, ensuring alignment with business objectives and adherence to industry best practices.
Develop Near Real-Time Systems
Lead critical initiatives to build our next-generation near real-time ecosystem, to enhance near real-time observability and alerting, leveraging Scala, Pub/Sub, Akka, and Dataflow on Google Cloud.
Build Scalable Pipelines
Architect and maintain large-scale batch data pipelines using Google Dataflow, BigQuery, and Airflow/Cloud Composer to handle high-volume, batch data processing.
Develop Core Capabilities
Enhance the experimentation platform with new capabilities such as experiment targeting and localized assignments at scale to reduce latency and improve developer experience.
Optimize Data Infrastructure
Drive efficiency and performance improvements across experimentation pipelines, frameworks, and query layers. Evaluate trade-offs in system design, balancing speed, scalability, cost, and accuracy.
Stay Current with Industry Trends
Research, evaluate, and integrate the latest advancements in experimentation methods, data analysis techniques, and cloud-based technologies to continually improve the platform.
Mentor and Guide
Provide technical leadership and support to junior engineers, fostering a culture of continuous learning and professional growth.
Collaborate on Experiment Analysis
Partner with marketers, analysts, and data scientists to build infrastructure that supports thousands of metrics and various statistical methods (e.g., t-tests, sequential testing, Bayesian analysis).
Qualifications
Bachelor’s or Master’s degree in Computer Science, Statistics, or a related field.
10+ years in software engineering, with a focus on data engineering and data architecture.
Proficiency in Scala, Python, and SQL.
Demonstrated success building and maintaining large-scale data pipelines using technologies such as Spark, Flink, Google Dataflow, BigQuery, or Airflow/Composer.
Familiarity with Python libraries for statistical analysis (e.g., Statsmodels, SciPy).
Deep understanding of software development lifecycle best practices, including agile methodologies.
Excellent communication, collaboration, and stakeholder management skills.
Proven ability to lead complex projects and mentor engineering teams.
Proven expertise in A/B testing methodologies and statistical concepts.
What we would like to see
Previous experience building analytics pipelines at experimentation platform at large-scale tech companies serving millions of users or vendors (e.g., Optimizely, Statsig, LaunchDarkly)
Knowledge of heterogeneous treatment effects and advanced statistical modeling techniques for experimentation.
Experience with adaptive experimentation or Bayesian optimization methods.
Benefits
Medical and Dental Coverage
Retirement Plan
Commuter Benefits
Wellness perks
Paid Time Off (Vacation, Sick, Baby Bonding, Cultural Observance, & More)
Education Perks
Paid Gift Week in December
Equal Employment Opportunity Credit Karma is proud to be an Equal Employment Opportunity Employer. We welcome all candidates without regard to race, color, religion, age, marital status, sex (including pregnancy, childbirth, or related medical condition), sexual orientation, gender identity or gender expression, national origin, veteran or military status, disability (physical or mental), genetic information or other protected characteristic. We prohibit discrimination of any kind and operate in compliance with applicable fair chance laws.
Credit Karma is also committed to a diverse and inclusive work environment because it is the right thing to do. We believe that such an environment advances long-term professional growth, creates a robust business, and supports our mission of championing financial progress for everyone. We offer generous benefits and perks with a single eye to nourishing an inclusive environment that recognizes the contributions of all and fosters diversity by supporting our internal Employee Resource Groups. We’ve worked hard to build an intensely collaborative and creative environment, a diverse and inclusive employee culture, and the opportunity for professional growth. As part of the Credit Karma team, your voice will be heard, your contributions will matter, and your unique background and experiences will be celebrated.
Privacy Policies:
Credit Karma is strongly committed to protecting personal data. Please take a look below to review our privacy policies:
GDPR Privacy Policy
U.S. Job Applicant Privacy Notice
#J-18808-Ljbffr