About the Role

.Monks is a digital-first marketing and advertising services company connecting the dots across content, data & digital media and technology services. Inspired by the connectivity and flexibility of technology APIs, .Monks’ single-P&L model offers brands seamless access to a nearly 6,000-strong team of digital talent organized across 57 talent hubs in 33 countries. 

With us, you'll find a diverse group of colleagues with different backgrounds and perspectives. We believe everyone has something of value to offer, and that sustaining a truly diverse, equitable and inclusive workplace begins with fostering an environment where people can be themselves, authentically, every day. We want to build something with the potential to change the heart of our industry, and we’d love to include your unique perspective.

Media Analytics

As .Monks continues to expand our Global Enterprise Analytics capabilities, we are looking for a forward-deployed data engineer to serve as a high-exposure individual contributor embedded directly within our client's business. In this role, your primary responsibility will be building, maintaining, and scaling production-level data pipelines and infrastructure within the client's ecosystem. You will architect robust data engineering solutions and write production-level code to ensure data integrity and scalability. While this is an engineering-first role, you will also work with the Data Science team to assist in their application of statistical modeling and machine learning to help turn raw data into actionable business decisions. This position requires a unique combination of deep technical engineering expertise and the business acumen to drive services development from within the client's business.

Responsibilities:

• Design, build, and maintain scalable, reliable, and automated data pipelines using SQL, Python, and Databricks to support enterprise analytics.
• Architect and optimize robust data models and infrastructure to ensure high data quality, integrity, and accessibility across the client's ecosystem.
• Partner closely with the Data Science team to operationalize their work, deploying statistical and machine learning models into production environments using DataOps best practices.
• Identify, design, and implement internal process improvements, including automating manual data processes and optimizing data delivery for scalability.
• Collaborate with cross-functional teams to identify business problems, gather requirements, identify data sources, and provide data-driven solutions.

The Ideal Candidate

You are a Data Engineer who approaches data engineering as a software engineering discipline. You have experience building reliable, scalable, and maintainable data platforms using modern cloud-native technologies and engineering best practices. You are a proactive problem-solver who thrives in ambiguity. You take ownership of the full development lifecycle, are driven to understand the broader environment you work in, and actively identify and solve technical challenges (such as data inconsistencies or pipeline optimizations) without being prompted. You are a strong communicator and effectively kick-start your projects, seeking in-process guidance rather than waiting for project deadlines.

Requirements:

We are looking for someone who is experienced and familiar with the following tools:

• Strong experience designing and building scalable data pipelines using modern cloud data platforms.
• Solid understanding of modern data architecture, including ELT, data lakes/lakehouses, data warehouses, and metadata-driven frameworks.
• Experience applying software engineering best practices to data development, including:
• Version control (Git)
• Code reviews and pull request workflows
• Modular, reusable, and testable code
• CI/CD pipelines
• Automated testing (unit, integration, and data quality tests)
• Infrastructure as Code
• Proficiency in Python and SQL, with a focus on clean, maintainable, and well-tested code.
• Experience with orchestration frameworks and workflow automation.
• Familiarity with data modeling, data governance, lineage, observability, and monitoring.
• Experience working in Agile teams and collaborating across engineering, analytics, and business stakeholders.
• Ability to design metadata-driven and configuration-driven solutions instead of hard-coded implementations.

The essentials:

• A Bachelor’s or Master’s degree in Computer Science, Statistics, Applied Mathematics, or a related quantitative field (or equivalent practical experience)
• 5+ years of experience in data engineering, data warehousing, or building data infrastructure for marketing and business applications
• Hands-on experience working with common ETL tools
• Expertise across programmatic display, video, native, and ad serving technology, as well as digital advertising reporting, measurement, and attribution tools
• Adept to agile methodologies and well-versed in applying DataOps methods to the construction of pipelines and delivery
• Demonstrated ability to effectively operate both independently and in a team environment
• Experience in the client/consulting workplace and capable of reprioritization based on evolving client needs
• Added Bonus: You have expertise in designing and deploying AI workflows directly into a client’s business environment

At Monks, we believe in fostering an environment where a diversity of perspectives can thrive. We proactively work to design hiring processes that promote equity and inclusion while mitigating bias. We celebrate diversity and are committed to building a team that reflects the communities we serve. We welcome and encourage qualified applicants, from all backgrounds, who are excited to contribute to our mission.  

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.