Data Analytics Translator

The Data & Analytics (D&A) Translator creates business value by identifying, translating and supporting business-driven data and analytics initiatives. The role acts as a bridge between business stakeholders and technical D&A specialists, ensuring that data-driven objectives are translated into actionable solutions that deliver measurable value.

Throughout the inspiration phase, the D&A Translator supports the business in identifying relevant use cases and prioritizing them based on feasibility, business value and strategic alignment. During the ideation and prototyping phase, they contribute to the development of minimum viable products (MVPs) and business cases. During implementation, they ensure that the solutions are successfully adopted by employees, integrated into existing workflows and processes, and monitored to measure their impact.

A key responsibility is to identify, understand and translate business needs and requirements into clear, actionable specifications that technical specialists can understand and implement. This requires a strong understanding of business objectives, organizational processes and domain-specific challenges, combined with knowledge of data and analytics concepts, methodologies and techniques.

Strong communication, stakeholder management and collaboration skills are essential to bridge the gap between business and technology. The D&A Translator also engages with senior leadership, including C-level executives and the board of directors, to support the development and execution of a data-driven vision, strategy and operating model.

Soft Skills

• Communication and presentation
• Facilitation and creative thinking
• Teamwork and collaboration
• Stakeholder and relationship management
• Storytelling and influencing

Data & Analytics Skills

• Understanding of descriptive, diagnostic, predictive and prescriptive analytics
• Knowledge of AI, machine learning and NLP
• Data management, integration (ETL) and metadata
• Data architecture and data structures
• Data governance, data quality and MDM
• Basic statistical knowledge, including correlation versus causation

Technology Skills

• Basic knowledge of Python, R and SQL
• Familiarity with data platforms and tools such as Snowflake, Dataiku and Collibra
• Business Intelligence tools, particularly Microsoft Power BI
• Familiarity with Generative AI and LLMs, such as OpenAI

Project Management Skills

• Project planning, coordination and deadline management
• Alignment of business expectations with technical delivery
• Coordination of ideation and prototyping activities