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# Data Engineer: Data Ops

- **Company:** Mindsource Inc
- **Location:** Cupertino, CA, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Sharing, Data Systems, Python (Programming Language), Operational Databases, Systems Development Life Cycle, Standard Sql, DataOps, Software Engineering, SQL Databases, Google Cloud, GitHub Copilot, Apache Spark, Software Troubleshooting, AI Coding Agents, Git, Apache Kafka, Claude, GPT, Data Pipelines, Docker
- **Published:** October 18, 2026
- **Apply:** https://www.dice.com/job-detail/0176c6f5-eac0-41bd-bea6-4ec2f7e4dd0b

## About the Role

* 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.

## Description

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.