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About The Role In this role, you'll work closely with product teams to ensure they effectively adopt the tools, frameworks, and processes provided by the Data Platform team. Enabling them to build scalable, efficient, and reliable data and ML solutions, and achieve their OKRs while championing best practices in Data Engineering. You'll help teams implement robust data pipelines, model deployment workflows, monitoring strategies, and cost-efficient practices to improve their data-driven capabilities.

At the same time, you'll act as a crucial bridge between product teams and the Data Platform team, gathering insights on real-world challenges, gaps, and pain points in the existing platform. By surfacing these issues and collaborating with the platform team, you'll contribute to the continuous improvement of our internal tooling and infrastructure, ensuring it better serves the needs of our engineers and data scientists. This is an opportunity to blend hands-on engineering with strategic impact, influencing both product success and the evolution of our data platform.

What you'll do

• Work directly with product teams to facilitate the effective adoption of the Data Platform team's tools, frameworks, and processes to achieve their Objectives and Key Results (OKRs).
• Guide teams in implementing robust data pipelines, reliable model deployment workflows, effective monitoring strategies, and cost-efficient practices.
• Break down complex problems and propose well-reasoned data system designs, considering scaling needs, maintenance simplicity, and appropriate tooling (OLAP vs. OLTP, Batch vs. Stream, etc.).
• Act as a critical feedback loop, gathering insights on real-world platform challenges, gaps, and pain points from product teams and communicating them to the Data Platform team for continuous improvement of internal tooling and infrastructure.
• Champion software engineering best practices, including code quality, maintainability, version control, and automation, with a proficiency in Python.
• Work closely with Data Scientists, Software Engineers, and Product Managers to align data initiatives with overarching business goals.
• Share knowledge and help develop engineering capabilities across the broader organization, including engineers outside the data domain.

About You

• Proven ability to design and break down complex data systems, with a focus on simplicity, maintainability, and scaling.
• Solid proficiency in Python and adherence to software engineering best practices.
• Knowledge of at least one distributed processing framework (e.g., PySpark), with a bonus for experience in streaming frameworks (e.g., Flink).
• Practical experience with Docker and Kubernetes.
• Experience with IaC tools, specifically Terraform.
• Good understanding of different storage types and when to use them (e.g., OLTP, OLAP, S3).
• Understanding of core computer science concepts including Data Structures, Algorithms, Time Complexity, and Distributed Processing.
• Demonstrated ability to work effectively across teams (data scientists, software engineers, PMs).
• Understanding the value proposition of data solutions and adopting a customer-focused, incremental delivery approach.

By submitting this application, I confirm that all the information given by me in this application for employment and any additional documents attached hereto are true to the best of my knowledge and that I have not wilfully suppressed any material fact. I confirm I have disclosed if applicable any previous employment with Cleo AI. I accept that if any of the information given by me in this application is in any way false or incorrect, my application may be rejected, any offer of employment may be withdrawn or my employment with Cleo AI may be terminated summarily or I may be dismissed. By submitting this application, I agree that my personal data will be processed in accordance with Cleo AI's Candidate Privacy Notice