Lead Data Engineer - Experimentation Platform

Job Description City: Santa Monica, CA Onsite/ Hybrid/ Remote: Hybrid (4 days onsite per week, no flexibility) Duration: 6 Months Rate Range: Upto $100/hr on W2 Work Authorization: GC, USC, All valid EADs except OPT, CPT, H1B Must Have: Python SQL Data Engineering ETL / ELT Apache Spark Databricks Snowflake Apache Kafka Apache Airflow Streaming Data Pipelines Data Modeling Data Warehousing / Lakehouse A/B Testing / Experimentation Platforms CI/CD for Data Pipelines Data Quality & Data Governance Cloud Data Platforms Responsibilities: Design and build scalable data platforms supporting experimentation and A/B testing. Develop batch and streaming data pipelines for large-scale user and product datasets. Build reusable datasets and frameworks for experimentation, analytics, and product measurement. Design dimensional data models and analytics-ready data products. Implement automated data quality, validation, monitoring, lineage, and governance. Build production-grade deployment pipelines with CI/CD and observability. Partner with Product, Engineering, Data Science, and Analytics teams to deliver scalable data solutions. Optimize data infrastructure supporting experimentation, personalization, and machine learning workloads. Mentor engineers and establish best practices for large-scale data engineering. Qualifications: Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field. 7+ years of experience in data engineering or large-scale data platforms. Strong experience with distributed data processing and cloud-based data architectures. Hands-on experience with Python, SQL, Spark, Databricks, Snowflake, Kafka, and Airflow. Strong understanding of data modeling, ETL/ELT, streaming architectures, and lakehouse concepts. Experience building experimentation, analytics, personalization, or ML data platforms. Experience implementing CI/CD, automated testing, monitoring, and data governance. Strong system design and architecture experience. Experience mentoring engineers and leading technical initiatives. Nice to Have: Experimentation platforms or A/B testing infrastructure. Causal inference or product analytics experience. ML feature engineering and model lifecycle pipelines. Infrastructure automation and observability. Subscription, streaming media, advertising, or consumer product experience. MS or PhD in a related technical field.