Data Engineer - DataOps

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We are seeking a capable, detail-minded Data Engineer with a strong Data Ops 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. 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).

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
.

Data Ops & 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.

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.

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.

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-Quality Checks (DQCs), custom monitoring pipelines, and troubleshooting tooling that the broader analytics team relies on.

Platform Governance: Manage asset life cycles (retiring unused objects), remediate poorly performing queries,…