Data Analytics/Engineer

Role Overview: Wolters Kluwer, a global leader in professional information services, is seeking a skilled individual to join their team as a Data Science/Analytics Engineer. In this role, you will play a crucial part in developing predictive models, analyzing data, and driving strategic insights to support key business decisions. Your responsibilities will include architecting scalable data solutions, leading data engineering initiatives, and collaborating with cross-functional teams to enhance data workflows and automation. Key Responsibilities: - Develop predictive models to forecast key sales and marketing metrics, solve complex business problems, and drive strategic insights. - Lead the end-to-end lifecycle of data science projects, including data preparation, feature engineering, model development, validation, and deployment. - Architect predictive modeling frameworks and guide model development by junior team members. - Analyze large, complex datasets to identify trends, patterns, and opportunities that inform decision-making. - Collaborate with finance and accounting teams to automate reconciliations, variance analysis, and error detection. - Lead initiatives leveraging Generative AI (GenAI) technologies to automate the generation of financial reports, narratives, and data summaries. - Drive data initiatives in collaboration with finance and business leaders to enhance reporting and analytics standards. - Advocate for a data-driven culture, promoting the value of analytics in decision-making and providing expert data engineering support to cross-functional teams. Qualifications Required: - Bachelors degree in data science/analytics, Engineering in Computer Science, or related quantitative field. Masters degree preferred. - At least 5+ years of experience in Data Analytics/Engineering with a proven track record of leading data engineering initiatives. - Proficiency in SQL and Python/PySpark for complex data transformations and automation. - Hands-on experience with cloud platforms (Fabric or AWS) and data warehouses (Snowflake or Synapse) for large-scale data integration and analysis. - Strong analytical skills, written and verbal communication, stakeholder management, and ability to lead cross-functional initiatives. (Note: Any additional details about the company were not included in the provided job description.) Role Overview: Wolters Kluwer, a global leader in professional information services, is seeking a skilled individual to join their team as a Data Science/Analytics Engineer. In this role, you will play a crucial part in developing predictive models, analyzing data, and driving strategic insights to support key business decisions. Your responsibilities will include architecting scalable data solutions, leading data engineering initiatives, and collaborating with cross-functional teams to enhance data workflows and automation. Key Responsibilities: - Develop predictive models to forecast key sales and marketing metrics, solve complex business problems, and drive strategic insights. - Lead the end-to-end lifecycle of data science projects, including data preparation, feature engineering, model development, validation, and deployment. - Architect predictive modeling frameworks and guide model development by junior team members. - Analyze large, complex datasets to identify trends, patterns, and opportunities that inform decision-making. - Collaborate with finance and accounting teams to automate reconciliations, variance analysis, and error detection. - Lead initiatives leveraging Generative AI (GenAI) technologies to automate the generation of financial reports, narratives, and data summaries. - Drive data initiatives in collaboration with finance and business leaders to enhance reporting and analytics standards. - Advocate for a data-driven culture, promoting the value of analytics in decision-making and providing expert data engineering support to cross-functional teams. Qualifications Required: - Bachelors degree in data science/analytics, Engineering in Computer Science, or related quantitative field. Masters degree preferred. - At least 5+ years of experience in Data Analytics/Engineering with a proven track record of leading data engineering initiatives. - Proficiency in SQL and Python/PySpark for complex data transformations and automation. - Hands-on experience with cloud platforms (Fabric or AWS) and data warehouses (Snowflake or Synapse) for large-scale data integration and analysis. - Strong analytical skills, written and verbal communication, stakeholder management, and ability to lead cross-functional initiatives. (Note: Any additional details about the company were not included in the provided job description.)