Data Scientist III

Key Responsibilities. Data Platform & Architecture. Design and implement scalable data architectures including data lakes and data warehouses using Azure ecosystem (e.g., Microsoft Fabric, ADLS). Define data models and storage strategies for structured and unstructured data. Data Pipeline Development. Build and manage end-to-end data pipelines (ETL/ELT) to ingest, transform, and serve large-scale datasets. Ensure reliability, performance, and scalability of data pipelines. Analytics Enablement. Develop and standardize analytics frameworks and reusable data models to support reporting, BI, and advanced analytics. Enable self-service analytics through curated datasets and semantic layers. AI/ML, Analytics & Storytelling: Working knowledge of machine learning techniques and analytics use cases, with the ability to operationalize models within data platforms. Strong capability in data storytelling , translating complex data into intuitive visualizations and actionable insights for business stakeholders. Hands-on Technical Leadership. Remain hands-on with tools such as Python, SQL, and MS Fabric for data processing and transformation. Guide the team on best practices in data engineering, modeling, and performance optimization. Team & Delivery Management. Lead and mentor a team of data engineers / analysts / data scientists. Drive project delivery across multiple initiatives, ensuring timelines and quality. Stakeholder Collaboration. Work closely with business, analytics, and engineering teams to translate requirements into scalable data solutions. Support downstream use cases including BI, reporting, and machine learning. Data Governance & Quality. Establish data governance standards, including data quality, lineage, access control, and cataloging . Ensure compliance with enterprise data policies. Qualifications. Experience: 8-12 years in data engineering / data science / analytics, with 2-4 years in a managerial role.