Architect

Job Summary

Architect role in a hybrid work model for a global media and entertainment client focusing on designing and implementing data and machine learning solutions using Azure Databricks and Azure Machine Learning. Role requires twelve to sixteen years of experience with strong domain understanding of media workflows, content lifecycles, and audience analytics to drive impactful business outcomes.

Key Responsibilities

• Design end-to-end data and analytics architectures on Azure Databricks to support high-volume media and entertainment content workflows and audience analytics use cases.
• Develop scalable data engineering patterns that ingest, transform, and curate media usage data in Azure Databricks to enable reliable reporting and experimentation.
• Coordinate with product and business teams from media and entertainment lines of business to translate complex requirements into clear data and machine learning solution designs.
• Guide teams in implementing reusable frameworks for feature engineering, model training, and batch and near real-time scoring using Azure Machine Learning.
• Define standards for code quality, observability, data validation, and security across Azure Databricks workspaces to ensure resilient and maintainable solutions.
• Create reference architectures and design blueprints that optimize storage, compute, and orchestration choices for typical media audience measurement, recommendation, and advertising analytics scenarios.
• Collaborate with data scientists to operationalize machine learning models on Azure Machine Learning, including pipelines, monitoring, and continuous improvement practices.
• Review and refine solution designs to ensure they align with enterprise architecture principles, regulatory expectations, and specific needs of media and entertainment markets.
• Provide technical guidance to implementation teams to resolve performance bottlenecks, data quality issues, and integration challenges across upstream and downstream platforms.
• Document architecture decisions, data models, and integration contracts in a clear and consumable manner to support efficient onboarding and ongoing operations for hybrid teams.
• Engage with stakeholders to evaluate new Azure platform capabilities and industry offerings, identifying opportunities to enhance media analytics and automation capabilities.
• Mentor junior practitioners in modern data engineering and machine learning engineering practices to build a strong delivery capability within the organization.
• Drive continuous improvement by analyzing production usage and customer feedback to refine architectures and deliver measurable impact on audience engagement and operational efficiency.

Qualifications

• Require twelve to sixteen years of experience in data engineering or analytics architecture with significant focus on cloud-native platforms for enterprise-scale solutions.
• Require strong hands-on experience designing and implementing solutions using Azure Databricks, including cluster configuration, performance tuning, and workspace governance.
• Require proven expertise in Azure Machine Learning, including creation of training pipelines, model registry usage, deployment endpoints, and monitoring practices for production models.
• Require solid understanding of media and entertainment domain, including content supply chains, audience measurement, recommendation engines, and advertising or subscription analytics.
• Require proficiency in designing secure and compliant data solutions with focus on data privacy, data residency, and protection of sensitive media and customer information.
• Nice to have experience in developing or governing MLOps practices, including automated testing, model lifecycle management, and integration with DevOps toolchains in Azure environments.
• Nice to have exposure to advanced analytics workloads such as personalization, churn modeling, and campaign optimization tailored to media and entertainment business processes.
• Nice to have experience collaborating with cross-functional teams in hybrid work models using modern collaboration tools and structured documentation practices to ensure clarity and alignment.