Job description

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-Quality Checks (DQCs), custom monitoring pipelines, and troubleshooting tooling that the broader analytics team relies on. Platform Governance: Manage asset lifecycles (retiring unused objects), remediate poorly performing queries, optimize materialized reflections, and support engine/orchestrator version migrations. Incident Response & Change Management: Own production incident response for assigned domain areas, author clear structured change plans and Root Cause Analyses (RCAs), and maintain operational runbooks. #LI-MF1 #LI-Hybrid What We Offer Benefits Excellent, full coverage medical, dental, and vision insurance Generous PTO and 15 company-wide holidays 401k with company contribution Paid parental leave Work-life balance with an emphasis on personal well-being Career growth in a disruptor space & entrepreneurial opportunities within the Monks network A globally diverse & inclusive culture with employee resource groups such as S4 Melanin, Pride.Monks, Cultura.Monks, and more! Authentic commitment to DEI efforts and sustainable growth. (Why Sir Martin Sorrell signed The Climate Pledge here !) This role is subject to our Return to Office (RTO) policy. If you reside within a commutable distance of one of our office locations, you will be expected to work from the office a set number of days per week. The specific details, including the number of required office days, will be in accordance with the company’s then-current RTO policy, which is subject to change from time to time. Monks has provided a compensation range that represents its good faith estimate of what Monks may pay for the position at the time of posting. Monks may ultimately pay more or less than the posted compensation range. The salary offered to the selected candidate will be determined based on job-related factors, but not based on a candidate’s sex or any other protected status. Salary Range: $130,000 — $150,000 USD About Monks Monks is the global, digital-first, data-driven, unitary operating brand of S4Capital plc, where our culture centers on how we show up for the work and for one another. That begins with the commitment to strive for mastery—taking deep pride in our craft and creating work that sets the bar. In everyday execution, we drive momentum by choosing bold action rather than waiting for certainty. Above all, our teams are stronger together, bringing diverse perspectives into a unified team that communicates with clarity, trust, and mutual respect. This commitment to craft and innovation translates directly into widespread industry acclaim: Monks was named a Contender in The Forrester Wave™: Global Marketing Services and a leader in the 2026 Gartner® Magic Quadrant™ for Global Digital Marketing Agencies. It ranks among Cannes Lions' Top 10 Creative Companies (2022-25) and remains the only partner featured in AdExchanger’s Programmatic Power Players list every year (2020-24). Named Adweek’s first AI Agency of the Year (2023) and The One Show’s inaugural AI Pioneer Organization (2025), Monks was also awarded Business Intelligence Group’s 2026 Excellence in Artificial Intelligence Award in the Human-Machine Interaction Team category. Additionally, Monks.Flow earned AI-based Marketing Solution of the Year at the 2026 AI Breakthrough Awards and a 2026 Global Generative AI Award in the Agentic Marketing Innovation category. Finally, Monks achieved a record-breaking number of FWAs and continues to hold the most of any partner. We are an equal-opportunity employer committed to building a respectful and empowering work environment for all people to freely express themselves amongst colleagues who embrace diversity in all respects. Including fresh voices and unique points of view in all aspects of our business not only creates an environment where we can all grow and thrive but also increases our potential to produce work that better represents—and resonates with—the world around us.

Requirements

Must-haves

5+ years of Data Engineering experience (or Software Engineering with a strong data focus) accompanied by expert-level SQL .
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.
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
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-Quality Checks (DQCs), custom monitoring pipelines, and troubleshooting tooling that the broader analytics team relies on.
Platform Governance: Manage asset lifecycles (retiring unused objects), remediate poorly performing queries, optimize materialized reflections, and support engine/orchestrator version migrations.

Nice-to-haves

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 Domain Context: Prior experience working with Sales, Finance, or supply chain data domains (e.g., actuals, sell-through, forecasting) is highly preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonjavadockerci/cdai

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Pto

Parental Leave