Intuit Credit Karma is looking to build and scale data systems for their experimentation platform to enable reliable, consistent, and timely experimentation insights across the company. This involves designing robust data models, pipelines, and governance practices to support thousands of metrics and diverse statistical methodologies.
Requirements
- 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).
- Proven expertise in A/B testing methodologies and statistical concepts.
- Knowledge of heterogeneous treatment effects and advanced statistical modeling techniques for experimentation.
- Experience with adaptive experimentation or Bayesian optimization methods.
- Leveraging Google Cloud technologies, including Cloud Composer for workflow orchestration, Dataflow for data processing, and BigQuery for data warehousing.
Responsibilities
- Define Technical Strategy: Provide the roadmap and architecture for the experimentation platform’s analytics infrastructure, ensuring alignment with business objectives and adherence to industry best practices.
- Develop Near Real-Time Platform: Lead critical initiatives to build our next-generation near real-time ecosystem, 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.
- 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).
Other
- 10+ years in software engineering, with a focus on data engineering and data architecture.
- 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.
- Bachelor’s or Master’s degree in Computer Science, Statistics, or a related field.
