Senior Manager, Data & AI

Title: Senior Manager of Engineering, Data & AI

Reports To: CTO

Location: Hyderabad, India

Work setup: Hybrid (minimum 3 days/week in the office)

Talent Systems, LLC is the leading technology solution provider for casting and auditioning to the entertainment industry. Casting directors and agents worldwide use Talent Systems portfolio of products to source and manage talent across film, television, commercials, theater and digital projects, powering an unparalleled, global casting software ecosystem.

We are headquartered in Los Angeles and operate in the US, Canada, UK, Australia and India. Our portfolio brands include Casting Networks, Spotlight, Cast It Systems, Cast It Talent, Casting Frontier, Staff Me Up, Cast It Reach & Tagmin.

Job Description

We are looking for an experienced and visionary Senior Manager Engineering, Data & AI to lead our data engineering, AI, and storage systems for a suite of cloud-based applications. This leader will own the strategy and execution for our big data platforms, AI/ML infrastructure, and storage systems, ensuring they are scalable, reliable, and built to support both current and future innovation needs. You will also play a central role in advancing our capabilities in GenAI, LLMs, Vector Databases, and other emerging AI technologies, while fostering a forward-looking culture of innovation.

Leadership and strategy:

• Define and execute the long-term strategy for data engineering and AI platforms in alignment with business goals.
• Drive innovation in Big data, AI/ML, and GenAI solutions, anticipating future opportunities and challenges.
• Serve as the company's thought leader on AI and data innovation, advising executive leadership on emerging trends and technology investments.
• Data Engineering & Platform Development:
• Lead the design, development, and operation of enterprise-grade data platforms (e.g., Snowflake, data lakes, streaming systems).
• Architect and manage high-scale storage systems for structured, semi-structured, and unstructured data.
• Establish and maintain robust data pipelines, ensuring high availability, reliability, and performance.

Data Warehousing & Analytics Enablement:

• Oversee the design, implementation, and optimization of modern data warehouse solutions, with Snowflake as the preferred platform.
• Champion the use of DBT (data build tool) to enable scalable, modular, and version-controlled transformations.
• Develop frameworks for data modeling, transformation, and semantic layers to ensure data is consistent, accurate, and analysis-ready.
• Partner with BI teams to deliver self-service analytics capabilities via Qlik or equivalent visualization tools, ensuring accessibility for technical and non-technical users.
• Implement and enforce data governance, lineage tracking, and documentation standards so employees can confidently use data in decision-making.
• Ensure data is discoverable, well-modeled, and accompanied by clear business definitions to support a truly data-driven culture.
• Drive initiatives to reduce time-to-insight for analysts and business users through automation, optimized queries, and efficient data delivery patterns.

AI & Advanced Analytics

• Oversee the implementation and scaling of AI frameworks, LLMs, Vector DBs, and GenAI technologies for real-world applications.
• Partner with product teams to integrate AI capabilities into customer-facing and internal tools.
• Ensure ethical and secure use of AI and data in compliance with regulations and best practices.

Collaboration & Global Partnership:

• Collaborate with platform engineering, product engineering, and analytics teams distributed across the globe.
• Build strong partnerships with finance and business stakeholders to deliver actionable insights and intelligent features.
• Align cross-functional teams on priorities, dependencies, and data-driven strategies.
• Operational Excellence:
• Drive automation and self-service capabilities to empower engineering and analytics teams.
• Monitor and continuously improve data reliability, scalability, and cost-efficiency.
• Implement governance, security, and compliance frameworks for data and AI systems.

Talent Development & Culture:

• Build, mentor, and scale high-performing global teams in data engineering and AI/ML operations.
• Foster a culture of innovation, continuous learning, and experimentation.
• Attract top engineering and AI talent to strengthen the organization's capabilities.

Experience

• 10+ years in data engineering or related fields, with at least 5 years in senior leadership roles.
• Proven track record with big data platforms such as Snowflake (preferred), Databricks, AWS Redshift, or similar.
• Significant experience with AI/ML frameworks, GenAI technologies, LLMs, and Vector Databases.
• Strong background in architecting and managing cloud-based data and AI platforms (AWS preferred).
• Experience leading globally distributed teams and collaborating with cross-functional partners.
• Skills
• Deep expertise in data warehousing, ELT pipelines, and analytics platform integration.
• Strong understanding of data modeling, semantic layers, and best practices for BI enablement.
• Deep expertise in data architecture, pipeline engineering, and cloud-native data solutions.
• Strong understanding of machine learning, deep learning, and generative AI technologies.
• Exceptional leadership, strategic thinking, and communication skills.
• Ability to balance visionary innovation with practical execution.