Datarisk

• Own the product from problem identification to value delivery; • Translate business objectives into clear problems, hypotheses, and solutions; • Define and track OKRs, product metrics, and impact KPIs; • Prioritize the backlog based on value, effort, risk, and real data; • Actively participate in defining the roadmap (short, medium, and long term); • Drive continuous discovery (user interviews, data analysis, and real feedback from CS, sales, and support); • Create hypotheses, validate solutions, and avoid unnecessary build; • Collaborate with Design on flows, wireframes, and rapid validations; • Write clear and concise PRDs; • Detail User Stories, acceptance criteria, and business rules; • Work daily with the engineering team (squads); • Monitor development, testing, rollout, and post-launch; • Ensure quality, correct scope, and value delivery; • Track metrics before and after launch; • Evaluate the real impact of deliveries; • Make data-driven decisions (not just based on intuition); • Adjust the product quickly when necessary; • Define MVPs for rapid Product validation.

• Act as a consultant on strategic business projects focused on data engineering using the Databricks platform; • Identify and implement best practices for high-volume data ingestion, ensuring performance and data availability; • Work on query optimization and data modeling to improve the efficiency of analytical queries; • Develop and maintain data pipelines (ETL/ELT) to integrate multiple sources, ensuring data quality and governance; • Design and implement Data Lakehouse architectures, applying the Medallion pattern (Bronze, Silver, Gold); • Support data modeling for modern Data Warehouses (Star Schema, Snowflake); • Explore best practices in Big Data environments, especially in the context of Databricks and Delta Lake; • Use open formats such as Delta Lake, Iceberg, Parquet and Avro for building and governing modern Lakehouse architectures; • Ensure data governance, including access control and versioning.

• Understand business problems and define analytical hypotheses; • Perform in-depth exploratory data analysis (EDA) and generate actionable insights; • Build and validate statistical and machine learning models; • Perform feature engineering, evaluate and improve models; • Integrate solutions with data engineering and technology teams; • Implement and maintain monitoring for models in production; • Document and communicate results to technical and business stakeholders; • Additional responsibilities for Senior: Make independent technical decisions. Lead complex, high-impact end-to-end projects. Mentor and review the work of mid-level and junior colleagues. Communicate with and influence senior management/executive stakeholders.

• Work on building predictive models; • Develop solutions to delight customers and drive the company's results.

• Act as a Consultant on consulting projects, analyzing the data platform, identifying opportunities and proposing improvements; • Design, develop and deploy data pipelines into the Data Lake (Big Data); • Integrate various partner systems via APIs; • Develop, test and maintain database architectures and real-time or distributed systems; • Plan and execute data cleansing and remediation activities.