IND Senior Leader, Data

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.

Key Responsibilities

Data & AI Platform Leadership

• 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.

Data Engineering & Pipelines

• 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.

AI, GenAI & Advanced Analytics Enablement

• 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.

Architecture, Governance & Security

• 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.

Stakeholder Partnership

• 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.

People Leadership & Delivery Excellence

• 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.

Required Skills & Experience

• 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.

Nice to Have

• 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).