Data Engineer - DataOps

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: 5+ years of Data Engineering experience (or Software Engineering with a strong data focus) accompanied by expert-level SQL . 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 . DataOps & Lakehouse Expertise: Experience with lakehouse architectures, query engines, Apache Iceberg maintenance (compaction, snapshot management), and platform upgrade/migration workflows. 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. Data Domain Context: Prior experience working with Sales, Finance, or supply chain data domains (e.g., actuals, sell-through, forecasting) is highly preferred. The Mindset We’re Looking For: Root-Cause Investigator: You don't just apply quick patches—you independently troubleshoot shared production issues, trace anomalies back to their upstream source, and implement durable, long-term fixes. Operational Discipline: You hold high standards for software lifecycle best practices, rigorous separation of dev and prod environments, careful change management, and comprehensive documentation. Platform Ownership: You take pride in data quality and system reliability across the entire team's pipeline estate, not just the code you personally authored. Autonomous & Adaptable: Comfortable stepping into ambiguous technical challenges, navigating complex data environments, and driving solutions to completion with minimal oversight. The Day-to-Day (Your Impact) Hands-On Data Engineering Build & Enhance Pipelines: Develop and maintain efficient ingestion and transformation pipelines from diverse, variable-quality data sources into curated aggregation layers, virtual views, and incremental-refresh logic. Workflow Orchestration: Own the code, business logic, and operational health (SLIs/SLOs) of your data products using Apache Airflow to orchestrate, schedule, and monitor workflows. Reusable Architecture: Contribute to and leverage shared utility libraries, local validation practices, and scalable coding patterns across the data engineering team. DataOps & Shared Platform Reliability DAG Hardening & Resilience: Drive platform-wide reliability by improving preflight cleanup, downstream-refresh stability, table/storage optimization, and failure-alerting flows across shared pipelines. Automated Data Quality & Observability: Design and deploy automated Data…