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City:Â Santa Monica, CA
Onsite/ Hybrid/ Remote:Â Hybrid (4 days onsite per week, no flexibility)
Duration:Â 6Months
Rate Range:Â Upto $100/hr on W2
Work Authorization:Â 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.
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