Leads the design and development of scalable batch and streaming data platforms for experimentation, A/B testing, analytics, personalization, and machine learning. Builds dimensional models, reusable datasets, data quality and governance systems, CI/CD pipelines, monitoring, and observability. Partners with product, engineering, data science, and analytics teams; optimizes cloud data infrastructure; mentors engineers; and establishes technical best practices.
City: Santa Monica, CA
Onsite/ Hybrid/ Remote: Hybrid (4 days onsite per week, no flexibility)
Duration: 6Months
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
- 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.
- 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.
- 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.
aKube Inc Los Angeles, California, USA Office
Los Angeles, California, United States
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What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
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- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
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- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
