Data Engineer

Open to both Cupertino, CA and Austin, TX locations! Core Responsibilities • Operations & Reliability • Lead day-to-day operational management of analytics infrastructure. • Ensure high availability , performance, and scalability of batch and real-time systems. • Drive zero-downtime deployments through CI/CD and release best practices. • Own incident management, production debugging, and post-incident reviews. • Infrastructure & Platform Enablement • Provision, enable, scale, and maintain data, analytics, and ML infrastructure in hybrid cloud. • Build tools for observability , monitoring, alerting, and self-healing. • Implement infrastructure-as-code and orchestration frameworks. • Ensure governance, compliance, and security best practices. • Automation & Efficiency • Develop self-service tooling to improve engineering productivity . • Drive cost optimization and infrastructure efficiency initiatives. Required Qualifications • 6+ years of experience in data engineering for analytics or ML systems. • Strong SQL proficiency . • Experience in Python, Scala, or Java. • Hands-on experience with Spark, Kafka, and Airflow (or similar). • Strong understanding of data modeling and lakehouse architectures (e.g., Iceberg). • Experience with AWS, Azure, or GCP . • Comfortable participating in rotating on-call. • Experience with Snowflake, Databricks, Trino, OLAP/NRT systems, Superset or Tableau. • Familiarity with CI/CD, data observability , infrastructure-as-code. • Exposure to MLOps and GenAI/RAG pipelines. • Hands-on experience with LLMs (prompt engineering, fine-tuning, RAG). • Experience in FinTech, Wallet, or Payments domain. Skill Prioritization & Ideal Background • Snowflake, Databricks, and Tableau are priorities. • Candidates from large, structured environments: • Are more likely to have deep experience with big data, streaming, Spark, ML Ops, etc. • Snowflake = soft requirement. • Other cloud DW experience (e.g., Redshift, BigQuery) is sufficient.