Data Engineer
Title: Data Engineer
Hybrid in Cupertino, CALong Term Contract Local Candidate only who can come for onsite Interview.
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.