• Strong experience with both Adobe Analytics • Experience in AA strategy and implementation expertise • Implementation experience with Adobe Launch • Understanding of web analytics tool basics: tags, cookies, variables. • Strong understanding of tag management systems: tags, rules, and variables • Experience in Vue.JS, JavaScript, jQuery, CSS and HTML skills, and able to be a leader in this area of expertise • Strong attention to detail and QA abilities • A solid understanding of advertising, marketing and strategic brand management and how to best leverage these in a digital environment • Experience presenting in front of groups, to clients, and via web conference • History of working with new business teams on requests for proposal/information and presenting analytics documentation demonstrating agency skillsets. • Experience in creating case studies, point of view documents or white papers in line with your job function in analytics. • Work with internal team to continually streamline processes and find efficiencies in the day-to-day work processes done by the data platforms team. • Experience working on testing, targeting and personalization projects with both targeting and optimization tools and data management platforms (DMP).

• Own the architecture and operations of our data lakehouse, including object storage, table formats, maintenance, and query engine integrations • Build and maintain the infrastructure layer that transforms and serves data reliably at scale—from raw landing zones through to curated, queryable datasets • Partner with product engineering to establish data contracts and schema standards around event telemetry, ensuring data arrives in the lakehouse in a form that's reliable and ready for downstream use • Drive decisions on data platform architecture, tooling, and engineering best practices across storage, compute, and access layers • Enhance observability and monitoring of data infrastructure, including pipeline reliability, data freshness, and system performance • Partner cross-functionally with teams across Analytics, Infrastructure, and Product to understand data needs and deliver impactful platform solutions • Provide product feedback by dogfooding new data infrastructure and AI technology

• Design, build, and maintain cloud-native data infrastructure using Terraform for IaC. • Develop and optimize data pipelines leveraging AWS services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora, etc.) and Snowflake. • Implement ELT workflows within dbt in partnership with our Analytics Engineering function. • Build and maintain LLM frameworks, ensuring high-quality and cost effective outputs. • Automate infrastructure and pipeline deployments with CI/CD best practices. • Monitor, debug, and improve system performance with strong observability and logging practices. • Partner with cross-functional teams (analytics, data science, product, engineering) to deliver high-quality data solutions. • Mentor teammates and contribute to engineering standards, raising the technical bar across the team.

At ClickUp, we’re not just building software. We’re architecting the future of work! In a world overwhelmed by work sprawl, we saw a better way. That’s why we created the first truly converged AI workspace, unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI, empowering millions of teams to break free from silos, reclaim their time, and unlock new levels of productivity. At ClickUp, you’ll have the opportunity to learn, use, and pioneer AI in ways that shape not only our product, but the future of work itself. Join us and be part of a bold, innovative team that’s redefining what’s possible! 🚀 We are seeking a highly skilled and motivated ML Engineer to join our team. This role sits at the intersection of machine learning, data science, and MLOps, requiring you to own the full lifecycle of ML systems — from feature engineering to model production deployment and monitoring. You will collaborate closely with data scientists, analysts, and data engineering teams to build robust, scalable ML systems that drive impactful business decisions. The Role - Model Development & Deployment: Deploy production-grade machine learning models, ensuring reliability, low latency, and scalability. - MLOps & Infrastructure: Build and maintain end-to-end ML pipelines, including automated training, evaluation, versioning, deployment, and monitoring workflows. - Feature Engineering: Partner with data scientists to design, implement, and optimize feature pipelines that feed into ML models, ensuring data quality and freshness. - Model Performance & Monitoring: Establish monitoring frameworks to track model performance, detect drift, and trigger retraining as needed. - Data Science Enablement: Work alongside data scientists to translate research prototypes into production-ready systems, and create tooling that accelerates experimentation. - Collaboration: Act as a bridge between data science and software engineering teams, ensuring seamless integration of ML models into broader product and platform architectures. - Performance Optimization: Continuously improve model inference speed, pipeline efficiency, and overall system scalability. Qualifications - Experience: 4+ years of experience in ML engineering, data engineering, or a related role, with at least 2 years focused on building and deploying machine learning systems in production. - Technical Skills: - Strong proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Hands-on experience with MLOps tools and platforms (e.g., MLflow, SageMaker, Kubeflow, Vertex AI). - Solid SQL skills and experience with data warehouses and feature stores. - Experience with big data technologies (e.g., Spark, Hadoop) and streaming frameworks. - Expertise in cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes). - Familiarity with CI/CD practices applied to ML workflows. - ML Knowledge: Strong understanding of machine learning algorithms, model evaluation techniques, feature engineering, and experiment tracking. - Soft Skills: Strong problem-solving abilities, excellent communication skills, and a collaborative mindset with the ability to work across technical and non-technical stakeholders. Desirable - Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field. Unsure if you meet all the qualifications of this job description but are deeply excited about the role? We hire based on ambition, grit, and a passion for improving the way people work. If you think ClickUp is the company for you, we encourage you to apply! At ClickUp, we assess every candidate based on the potential impact they can have. We hire the best people for the job and support each person’s journey to build their boldest career. Equal Opportunity Employer ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin. Privacy Notice ClickUp collects and processes personal data in accordance with applicable data protection laws. - If you are a European Job Applicant, see our privacy policy for further details. - If you are a Philippine Job Applicant, see our privacy policy and our Philippine Data Privacy Notice for further details. Visa Sponsorship Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions. Fraud Alert ClickUp Talent Acquisition will only initiate contact via an @clickup.com email or through our official careers portal on clickup.com. We will never request fees, payments, or sensitive personal information. Please disregard any offers received outside these channels and report them to support@clickup.com.