IND Senior Leader, Data

IND Executive Director, Data - GCC131
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

Position Overview

We are seeking a highly accomplished Senior Leader - Data to help lead and execute the organization's enterprise Data, Analytics, and Artificial Intelligence strategy. Reporting directly to the Head of Data & AI, this role will be responsible for translating strategic data and AI priorities into scalable platforms, high-impact data products, and production-grade AI solutions.

The role requires deep expertise in modern data engineering, cloud-native architectures, real-time and streaming platforms, and AI-ready data pipelines, combined with strong people leadership and cross-functional collaboration skills. The Senior Director will oversee critical data domains, lead large engineering teams, and ensure the delivery of reliable, governed, and business-aligned data and AI capabilities.

• Lead and scale enterprise data engineering and AI platform initiatives, aligning execution with the broader enterprise Data & AI strategy.
• Own the design, build, and operation of modern data platforms, including data lakes, lake houses, warehouses, and real-time streaming ecosystems.
• Drive adoption of cloud-native data architectures and engineering best practices across large and complex data environments.

• Oversee end-to-end data ingestion, transformation, enrichment, and orchestration pipelines supporting analytics, data science, AI/ML, and GenAI use cases.
• Lead implementation of AI-ready data pipelines, including:
  • Structured, semi-structured, and unstructured data processing
  • Metadata management, data quality, and observability
  • Scalable batch and streaming data processing
• Ensure high standards for data reliability, availability, performance, and scalability.

• Partner closely with Data Science and AI teams to enable Generative AI, RAG, and Agentic AI solutions through robust data foundations.
• Support semantic modeling, embeddings, metadata strategies, and vectorized data access for AI and conversational analytics platforms.
• Enable advanced analytics and real-time insights through optimized data access patterns and low-latency architectures.

• Enforce enterprise data architecture standards, design patterns, and technology guardrails.
• Drive strong data governance, lineage, cataloging, security, and compliance, ensuring responsible and ethical use of data.
• Collaborate with Security, Infrastructure, and Architecture teams to ensure secure, compliant, and resilient data platforms.

• Act as a key partner to business, product, and technology leaders to translate business needs into scalable data and AI solutions.
• Communicate complex technical concepts clearly, linking data and AI initiatives to measurable business outcomes.
• Support roadmap planning, prioritization, and execution governance.

• Lead and mentor large, high-performing teams of data engineers, platform engineers, and technical leaders.
• Foster a culture of engineering excellence, innovation, ownership, and continuous improvement.
• Drive agile delivery practices, strong execution discipline, and predictable outcomes across multiple parallel initiatives.

• Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
• 19 to 24 years of progressive experience in data engineering, data platforms, or large-scale data architecture.
• Proven experience leading enterprise-scale data engineering teams and complex, cloud-native data platforms.
• Deep expertise in:
  • Data lakes, lake houses, data warehouses, and streaming platforms
  • Real-time and batch data processing architectures
  • Data products, data domains, and analytics enablement models
• Strong hands-on or architectural experience with:
  • SQL and NoSQL databases
  • Big data ecosystems (Spark, Kafka, equivalent)
  • Cloud platforms (AWS, Azure, or GCP)
• Experience enabling data platforms for AI/ML and Generative AI use cases.
• Strong understanding of data governance, security, quality, and compliance in enterprise environments.
• Excellent leadership, communication, and stakeholder management skills.
• Ability to operate effectively in fast-paced, agile, and matrixed enterprise environments.

• Experience with Snowflake in large, enterprise-scale implementations.
• Exposure to vector databases, semantic layers, or knowledge graphs.
• Experience in regulated industries such as Insurance, Financial Services, or Healthcare.
• Cloud or data platform certifications (AWS, Azure, GCP, Snowflake).