Architect (Data Analytics)

Key Skills

Agile methodologies, API, Azure, C# Programming, Data Analytics, Data Architecture, Data Lake, Data science techniques, Data Visualization, Data Warehousing, ETL frameworks, Operations, PowerBI, Python Programming

Job Description

• Architect simple and effective solutions, based on business requirements, the current and future data platform and the latest standards in data and analytics, design and plan how to deliver them;
• Provide technical knowledge and guidance to product managers, software developers, data scientists;
• Plan new technology deployments and provide technology Proof of Concepts to evaluate and validate the potential of new technologies to help leverage data, with a strong focus on creating highly reusable data assets, data quality, observability, and discoverability;
• Define KPIs and collect metrics to measure progress towards achieving our analytical vision;
• Technology observation – research new/emerging technologies that could benefit predictive analytics solutions;
• Spread innovation and best practices within and outside the team, creating a community of technology experts, organizing workshops, demonstrations and training;
• Carry out capacity planning;
• Identify dependencies in parallel execution projects in the DT&A portfolio and involve the analytical scope;
• Work closely with DT&A partners from other service lines, architects and highlight cross-functional issues or synergies.

Experiences

• Experience in at least one reference programming language (preferably Python or C#) and in architecting software solutions;
• Experience in architecture for self-service data and analytics platforms in the cloud, ideally based on the MS Azure framework;
• Experience in data modeling;
• Experience in data integration, with ETL technologies and API implementations;
• Experience in designing and implementing data lake, data warehouse and database;

Any of the following considered an advantage:

• Experience in the commodities sector, preferably agribusiness;
• Experience supporting a data science platform;
• Agile structure, product mindset;
• Data visualization tools, preferably PowerBI;

Skills

• Big data techniques: high-performance computing, spark, scalable solutions.

Required Languages

• English (Basic)

Required Education

Completed Higher Education in Computer Science, Business/Management Information Systems, Computers/Systems/Industrial Engineering, Business Analysis, Data Science, Operations Research or Statistics.