Data Engineer
Posted 12 Aug 2026
Advertised as “Data Engineer - DataOps”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Contract
No salary listed for this role.
Please note that we will never request payment or bank account information at any stage of the recruitment process. As we continue to grow our teams, we urge you to be cautious of fraudulent job postings or recruitment activities that misuse our company name and information. Please protect your personal information during any recruitment process. While Monks may contact potential candidates via LinkedIn, all applications must be submitted through our official website (monks.com/careers).
Data Engineer
About the Role
We are seeking a capable, detail-minded Data Engineer with a strong DataOps focus to join our team. In this role, you will be embedded at Monks, serving as a critical delivery partner dedicated to supporting one of our most prestigious, high-profile global client accounts in Cupertino, CA.
You will build, scale, and maintain the core data pipelines that deliver trusted sell-through, actuals, and strategic business data to global Sales & Finance leadership. Beyond building your own pipelines, you will play a crucial role in hardening, monitoring, and troubleshooting the team's shared pipeline estate—ensuring high reliability, data quality, and platform performance across production.
Note on Location & Structure: This is a contract role based out of Cupertino, CA, with a hybrid schedule requiring you to be on-site 3 days per week (Tuesday – Thursday).
About You
The Skills & Toolkit You’ll Bring:
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5+ years of Data Engineering experience (or Software Engineering with a strong data focus) accompanied by expert-level SQL.
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Core Tech Stack: Advanced proficiency in Python (Java or Scala is a plus), paired with hands-on experience using Airflow, Spark, Trino/Dremio, Apache Iceberg, Kafka, and Docker.
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DataOps & Lakehouse Expertise: Experience with lakehouse architectures, query engines, Apache Iceberg maintenance (compaction, snapshot management), and platform upgrade/migration workflows.
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Production Pipeline Management: Proven track record designing, deploying, and maintaining custom ETL/ELT pipelines and enterprise data warehouse solutions using version control (Git) and CI/CD pipelines.
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Data Domain Context: Prior experience working with Sales, Finance, or supply chain data domains (e.g., actuals, sell-through, forecasting) is highly preferred.