P
Data Engineer
Salary & Market Data
Matched to BLS occupational data · California
Job Description
Key Responsibilities
- Build and maintain batch and real-time data pipelines supporting Data Science, analytics, and operational use cases.
- Develop scalable data models, ETL/ELT pipelines, and distributed processing jobs across structured and unstructured data.
- Implement ingestion, transformation, streaming, storage, and data quality solutions using Spark, Kafka, Python, and modern data frameworks.
- Partner with product, engineering, analytics, and data science teams to deliver reliable, privacy-aware, and cost-efficient data platforms.
- BS/MS in Computer Science, Engineering, Data Science, or related field.
- 5 8 years in data engineering, software engineering, or platform engineering with strong experience building scalable data pipelines and distributed systems.
- Strong proficiency in Spark and Python, with hands-on experience in production-grade data engineering and cloud-based data platforms.
- Hands-on with Spark, Kafka, HBase, Presto, Hive Flink, Airflow/Beam, SQL/NoSQL, cloud platforms, and AI/ML data enablement.
- Good-to-have experience in AdTech e.g. Google Ads, digital advertising, retail media, audience platforms, or marketing measurement.
- Exposure Data science models, recommendation systems, experimentation, A/B testing, or real-time decisioning.
- Knowledge of Data privacy, data governance, Kubernetes, Docker, and microservices.
- Ability to interpret performance metrics, conduct A/B testing, and use analytics tools like Google Analytics 4 (GA4) to track user behavior and Return on Ad Spend (ROAS)
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