Data Engineer: Data Ops

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.

• 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 Google Cloud Platform.
• Experience with Sales or Finance analytics.
• Experience using AI coding tools such as Claude, ChatGPT, or GitHub Copilot.

Mindsource is seeking a Data Engineer for one of our Direct Clients based in Silicon Valley . If interested, please drop your resume to