Data Engineer

Team Responsibilities

• Defining and setting up advanced technological solutions, strategies and standards to manage the overall Company data, to constantly improve automated data exchange within the IT platforms and AI solutions architecture
• Promoting and managing the transition of the incumbent IT architecture to selected Cloud ecosystems
• Evaluating the architectural consistency of all the “digital touchpoints”
• Supporting in developing new business/organizational models based on cloud digital platforms, to leverage big data analytics, machine learning capabilities and help the business grow/optimize costs
• Defining, setting up and managing technology standards and solutions aimed mainly at calculation engines for analytics, Data Warehousing and Business Intelligence/Reporting tools
• Managing the key Data Technology and Reporting Solutions taking care of their seamless operational functioning
• Taking care of the development of data architectures functional to business needs, and of their implementation through state-of-the-art technologies and infrastructures
• Managing requirements gathering and analysis, technical solution design and implementation, maintenance and evolution of related systems and of the other data technology and reporting solutions and their related technological and infrastructural framework
• Steering and managing the relationship with the external IT vendors providing data technology and reporting solutions and services to the Company, overseeing the service levels delivered and the consistency of the solutions; proposed with the overall technology and systems framework of the Company

Selected candidate will:

• Design, build, and maintain scalable data pipelines to support business analytics, machine learning models, and AI-driven decision‑making.
• Develop and optimize ETL processes, ensuring efficient data ingestion from multiple sources (structured and unstructured) into cloud-based data lakes and warehouses.
• Implement data quality frameworks and monitoring systems to ensure high data reliability, consistency, and governance.
• Integrate machine learning workflows by developing feature stores, automating data preprocessing pipelines, and optimizing model training datasets.
• Work closely with MLOps teams to enable the deployment, monitoring, and lifecycle management of machine learning models in production.
• Collaborate with Cloud Data Platform engineers to optimize storage solutions, data processing frameworks, and cost‑efficient cloud architectures.
• Ensure security and compliance with industry best practices and regulatory standards related to financial data.
• Provide technical leadership on emerging technologies, data engineering best practices, and scalable AI integration strategies.
• Coordinate with external resources on developing complex solutions in the data and machine learning domains.

Requirements

• 3+ years of experience in Data Engineering or Machine Learning Engineering
• Strong proficiency in Python (Pandas or similar, PySpark), SQL, or Scala for data manipulation and transformation
• Experience with cloud platforms such as AWS, Azure, or Google Cloud and data storage technologies (Redshift, DynamoDB, BigQuery, etc.)
• Hands‑on experience with distributed computing frameworks (Spark, Ray, etc.)
• Solid understanding of data modeling, data contracts, and governance frameworks
• Experience with MLOps tools and frameworks such as MLflow, Kubeflow, and Vertex AI
• Familiarity with machine learning model operationalization and feature engineering
• Bachelor’s degree in Computer Science, Engineering, or a related field

Nice to Have

• Master’s degree or PhD in Data Science, AI, or a related field
• Experience in the financial or asset management sector
• Familiarity with BI tools such as Power BI, Tableau, or Looker
• Understanding of regulatory requirements in financial data governance
• Excellent communication and leadership skills
• Pragmatic engineering approach to solve problems
• Collaborative mindset with a focus on driving business value through data
• Strong problem‑solving abilities and capacity for abstraction
• Ability to manage multiple projects and deadlines in a fast‑paced environment
• Ability to develop knowledge in new technologies and good proactivity