Senior Data Engineer

Senior Data Engineer, Data Management & BI Location: New York, New York, United States (Remote) Industry: Entertainment Providers Work Setting: Remote (Continental U.S.) with occasional travel. Are you passionate about building scalable data platforms and engineering solutions that transform complex data into actionable business insights? Join a collaborative team where you'll play a key role in designing, developing, and optimizing modern data ecosystems that support analytics, reporting, and strategic decision-making across the organization. As a Senior Data Engineer, you will: Design, develop, deploy, and maintain scalable data pipelines that process structured and unstructured data from multiple sources. Translate business requirements into robust technical solutions, including data models, integration frameworks, and data architectures. Build high-quality, reusable, and maintainable code with a focus on reliability, performance, scalability, and observability. Develop and optimize batch, real-time, and cloud-native data processing solutions. Contribute to architectural decisions involving data warehouses, data lakes, APIs, orchestration frameworks, and integration platforms. Implement data validation, monitoring, alerting, and operational support processes to ensure data accuracy and availability. Design and enhance automated deployment workflows, CI/CD pipelines, testing strategies, and infrastructure automation practices. Partner with analytics, reporting, product, and business teams to deliver trusted and well-documented data products. Evaluate source systems, APIs, and data flows to identify opportunities for optimization, risk reduction, and improved efficiency. Participate in Agile development activities, including sprint planning, code reviews, release management, and continuous improvement initiatives. Mentor team members and advocate for engineering best practices across development, testing, documentation, and support processes. Create and maintain technical documentation, architecture artifacts, implementation guides, and data lineage information. What we're looking for: Bachelor's degree in Computer Science, Information Systems, Engineering, Data Analytics, or a related discipline, or equivalent practical experience. 5+ years of hands-on experience building, deploying, and supporting production-grade data engineering solutions. Strong expertise in data modeling, data architecture, ETL/ELT frameworks, data quality management, and modern analytical data platforms. Experience working with cloud-based data services and distributed data processing environments. Proficiency in programming languages commonly used for data engineering, automation, and large-scale data processing. Experience working with modern data platforms, data warehouses, lakes, and distributed processing technologies. Strong understanding of source control, CI/CD practices, automated testing, and deployment automation. Knowledge of DevOps principles, production support, monitoring, troubleshooting, and performance optimization. Excellent analytical and problem-solving abilities with experience resolving complex technical and data challenges. Strong communication skills with the ability to engage effectively with both technical and non-technical stakeholders. Preferred Qualifications: Experience working in highly data-driven environments with complex reporting and analytics requirements. Expertise integrating data from APIs, files, event streams, relational databases, and cloud-based data platforms. Familiarity with workflow orchestration and data pipeline management tools. Understanding of infrastructure automation, cloud security, identity management, and secure engineering practices. Experience supporting business intelligence and reporting solutions. Knowledge of designing high-performance data models and curated datasets for analytics and operational use cases. Familiarity with data governance, lineage tracking, observability, and data quality frameworks. Ability to thrive in ambiguous environments, identify dependencies, and drive projects from concept to production. Strong sense of ownership with the ability to balance delivery timelines, engineering excellence, and stakeholder expectations. Proven ability to collaborate across engineering, product, operations, analytics, and business teams. Interest in emerging technologies, data automation, and AI-assisted development practices that enhance engineering productivity and solution quality.