Lead Product Software - Data Science Engineer @ Wolters Kluwer

Job Description

Job Description Summary Designs, develops, tests, debugs and implements more complex operating systems components, software tools, and utilities with full competency. Coordinates with users to determine requirements. Reviews systems under development and related documentation. Makes more complex modifications to existing software to fit specialized needs and configurations, and maintains program libraries and technical documentation. May coordinate activities of the project team and assist in monitoring project schedules and costs. Essential Duties And Responsibilities Lead and Manage configuration, maintenance, and support of portfolio of AI models and related products. Manage model delivery to Production deployment team and coordinate model production deployments. Ability to analyze complex data requirements, understand exploratory data analysis, and design solutions that meet business needs. Work on analyzing data profiles, transformation, quality and security with the dev team to build and enhance data pipelines while maintaining proper quality and control around the data sets. Work closely with cross-functional teams, including business analysts, data engineers, and domain experts. Understand business requirements and translate them into technical solutions. Understand and review the business use cases for data pipelines for the Data Lake including ingestion, transformation and storing in the Lakehouse. Present architecture and solutions to executive-level. Minimum Qualifications 5+ years of overall experience in Data Science Consistent record in building data science products using modern development lifecycle methodologies: CI/CD, QA, and Agile Methodologies Expert level knowledge of Python programming language, data engineering and data science ecosystem in Python. Production level Python script coding experience is must have. Expert in End-to-end design and architecture of enterprise level data warehouse lakehouse/bigdata solutions, data lifecycle management, Data transformation pipelines Some Experience with Dimensional modeling and data warehouse design methodologies. Experience with Cloud/Infrastructure provisioning, security, and best practices with Azure data platform. Experience with Azure data platform (Azure data factory, Azure data lake, Azure SQL DB, Azure DW/Synapse) or similar AWS/Google cloud-based services. Experience with Spark, DataBricks, Power BI, Azure Analysis Services Experience with Data integration and streaming tools like Azure stream analytics & event hub. Experience with working with business stakeholders, requirements & use case analysis. Strong communication and collaboration skills with creative problem-solving skills. Preferred Qualifications Bachelor's degree in Computer Science or equivalent work experience. Experience with Agile/Scrum methodology. Experience with tax and accounting domain a plus. Azure Data Scientist certification a plus.,

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Keyskills:   Data engineering QA Agile Methodologies Data warehouse design Spark Power BI Data integration Business analysis Dimensional modeling Python programming