Data Engineer - DataOps

monks; Cupertino (CA); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Excellent medical, dental, and vision

PTO and holidays

401k with company contribution

Paid parental leave

Career growth opportunities

Diverse and inclusive culture

Job summary

Monks is seeking a Data Engineer with a strong DataOps focus to join our team in Cupertino, CA on a contract basis. You will embed with a high-profile global client account, building and scaling core data pipelines that deliver trusted sell-through, actuals, and strategic business data to Sales & Finance leadership.

You'll own the data product lifecycle, harden pipelines for reliability, monitor performance, and collaborate with cross-functional teams to ensure high data quality and platform

Qualifications

• 5+ years of Data Engineering experience (or Software Engineering with a strong data focus) accompanied by expert-level SQL.
• Core Tech Stack: Python (Java/Scala a plus) with Airflow, Spark, Trino/Dremio, Iceberg, Kafka, and Docker.
• DataOps & Lakehouse: lakehouse architectures, query engines, Iceberg maintenance, and upgrade workflows.
• Production Pipelines: design, deploy, and maintain ETL/ELT pipelines and data warehouses with Git and CI/CD.
• Data Domain Context: experience with Sales, Finance, or supply chain data preferred.

Responsibilities

• Build and maintain ingestion and transformation pipelines from diverse data sources.
• Own orchestration and monitoring of workflows using Apache Airflow.
• Contribute to shared utilities and scalable coding patterns across the team.
• Improve data quality, observability, and platform reliability across pipelines.
• Collaborate with Sales, Finance, and supply-chain data domains.

Skills

Python

SQL

Airflow

Spark

Kafka

Docker

DataOps

ETL/ELT

DataLakehouse

CI/CD

Tools

Git

CI/CD pipelines

Airflow

Docker

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.

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, Prid