Data Engineer – DataOps
Location: Cupertino, CA (Hybrid – Onsite)
Job Summary
We are seeking a Data Engineer with a strong DataOps background to build, maintain, and support large-scale data pipelines powering Sales and Finance analytics. The ideal candidate will have expertise in SQL, Python, Airflow, Spark, and modern data platforms, along with hands-on experience supporting production data pipelines and resolving complex issues.
Key Responsibilities
- Design, develop, and maintain scalable ETL/data pipelines and warehouse solutions.
- Build and orchestrate workflows using Airflow.
- Develop data solutions using Python, SQL, Spark, Kafka, Trino/Dremio, Iceberg, and Docker.
- Monitor, troubleshoot, and support production pipelines, including incident response, backfills, and deployments.
- Perform root cause analysis (RCA) and resolve issues across shared data platforms.
- Implement data quality checks, monitoring, and validation frameworks.
- Collaborate with Data Engineering, Sales, and Finance teams to deliver reliable data solutions.
- Maintain Git, CI/CD, and SDLC best practices.
Required Qualifications
- 5+ years of Data Engineering or Software Engineering experience with a strong data focus.
- Strong SQL and Python skills.
- Hands-on experience with Airflow, Spark, Kafka, Trino/Dremio, Iceberg, Docker, and ETL/data pipeline development.
- Experience supporting production data pipelines, DataOps, incident management, and data quality.
- Strong troubleshooting, documentation, and communication skills.
Preferred Qualifications
- Java or Scala experience.
- Experience with AWS, Azure, or GCP.
- Experience with Sales or Finance analytics.
- Experience using AI coding tools such as Claude, ChatGPT, or GitHub Copilot.