Data Engineering Specialist
Mercer
Remoto
We will count on you to:
- System Design and Development: Designing and building robust, scalable data processing systems that can handle large volumes of data. This includes databases, data lakes, and other big data infrastructures;
- Data Pipeline Architecture: Overseeing + hands on in creation and maintenance of efficient data pipelines, which includes tasks such as data extraction, transformation, and loading (ETL);
- Data Management: Implement data strategies and develop physical and logical data models;
- Data Optimization: Develop and implement data optimization techniques to improve system efficiency and reduce data latency, complexity, and redundancy;
- Problem Solving: Use analytical skills to solve complex problems associated with database development and management;
- Collaboration: Working with other teams, such as data scientists, business analysts, and Qlik Developers, to identify organizational needs and design effective solutions;
- Innovation: Keeping up-to-date with new technologies and methodologies in the field of data engineering, and fostering a culture of innovation and continuous improvement within the team;
- Stakeholder Communication: Communicate effectively with both technical and non-technical stakeholders, explaining data infrastructure, strategies, and systems in an understandable way.
- Bachelor’s degree in Business Administration, Maths, Engineering, Economics or similar;
- Advanced Machine Learning Expertise – at least 4 years of strong hands-on experience with core ML algorithms (regression, classification, time-series forecasting) and ability to design, train, and optimize models for real-world problems;
- Forecasting Project Experience – Proven track record for more than 4 years into building and deploying forecasting models (demand, sales, or time-series) using Python libraries like Pandas, NumPy, Scikit-learn, and stats models;
- Python Proficiency – Deep knowledge of Python for data analysis, model development, and pipeline automation; experience with clean, modular, and production-ready code;
- Data Bricks experience;
- Ability to independently handle the full ML lifecycle: data preprocessing, feature engineering, model building, validation, deployment, and monitoring;
- Demonstrates curiosity and adaptability, proactively upskills on emerging technologies, tools, and frameworks in ML and data science;
- Ability to connect technical solutions with business goals, especially in forecasting and decision-making scenarios;
- Excellent Communication Skills;
- AI Automation - Develop and maintain AI-driven automation solutions to streamline operational tasks and management information (MI) processes. And integrate internal AI tools with existing systems to enhance data processing and reporting capabilities;
- Fluent English proficiency;
- Strong quantitative and analytical skills with ability to translate data into meaningful insights.
- Experience on leading BI tools like Qlik, Power BI or any other industry preferred BI tool;
- Aptitude for fostering positive relationships;
- Teamwork and leadership skills.
- We help you be your best through professional development opportunities, interesting work and supportive leaders.
- We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.
- Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.
Marsh is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.
Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.
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