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.
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
