Senior Lead Data Engineer, Content Engineering
remoterocketship
- United States
- United States
Über
Build & Operate Large-Scale Feature Pipelines: Design and maintain batch/streaming pipelines (Spark, Flink, Databricks, Airflow) producing ML features for ranking models. Ensure Point-in-Time Correctness: Develop feature sets that enable unbiased offline training and credible online inference. Develop Embedding & Content Pipelines: Build scalable workflows for metadata, imagery, and multimodal representations; partner with Science teams to operationalize new models. Architect Data Foundations: Design Delta/Parquet data models and medallion layers, optimizing storage layout and partitioning for latency and cost. Real-Time Engineering: Build Kafka-based systems for real-time features and user-activity aggregations, ensuring robust handling of out-of-order events and exactly-once semantics. Governance & Leadership: Define data quality rules and schema evolution processes while collaborating across ML pods to translate model needs into infrastructure. Requirements:
7+ years of experience in large-scale data or software engineering Deep experience with Spark (PySpark/Scala), Databricks, Airflow, and Kafka. Proficiency in feature pipelines, temporal joins, and mitigating training-serving skew. Experience with AWS/Azure/GCP and high-performance engines like Snowflake or Redshift. Proficient programming skills in Python and SQL with a focus on performance optimization. Experience in personalization domains (search, ranking, or recommender systems). Experience supporting petabyte-scale data lakehouses or feature stores. Familiarity with GenAI/RAG systems, multimodal content, or Delta Live Tables. Knowledge of Causal Inference, experimentation signals, or ML evaluation workflows. Experience with Terraform for governed, repeatable deployments. Benefits:
medical dental vision 401(k) plan life insurance coverage disability benefits tuition assistance program PTO
Sprachkenntnisse
- English
Hinweis für Nutzer
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