Data Solutions Consultant

Future Processing is a company with 25 years of experience in IT, focusing on data solutions and cloud technologies.

• Minimum 7 years of professional experience in IT, including at least 5 years focused on Big Data and cloud solutions
• Experience working directly with business and technical clients, with the ability to translate technical complexities into business benefits
• Advanced knowledge of Microsoft Azure cloud and practical proficiency in SQL
• Very good knowledge of Databricks platform and other modern tools for processing and analyzing large data sets (e.g., Apache Spark, Azure Synapse Analytics, Snowflake, Microsoft Fabric)
• Deep understanding of Lambda and Kappa architectures and modern data modeling concepts (Delta Lake, Data Lakehouse)
• Ability to design complete technical solutions considering advantages, disadvantages, and limitations of technologies
• Highly developed soft skills, ease in building relationships, excellent communication, and confidence in presenting to C-level management
• Fluent Polish and English (minimum C1) enabling smooth cooperation with clients from Europe and the USA
• Knowledge of MS Fabric, Databricks, Azure, and SQL

• Conduct advanced discussions with current and potential clients to translate business needs into Big/Cloud Data projects (consultative role without development tasks)
• Provide comprehensive technological advice on selecting optimal cloud architectures and tools considering costs, risks, and business benefits
• Design modern Big/Cloud Data solutions in collaboration with internal engineering teams, architects, analysts, and UX teams
• Actively support sales processes (pre-sales) by delivering expert knowledge, preparing alternative technical concepts, and estimating infrastructure costs (ROI, TCO)
• Perform audits and analyses of client organizations to identify technical and business opportunities and threats
• Co-create long-term strategy, development vision, and commercial offering of the Data Solutions business line within the company
• Build, integrate, and maintain a community of data engineers inside the organization and stimulate continuous knowledge exchange