Director, Data Engineering

Kroll is a global leader in risk and financial advisory solutions, and they are seeking a Director of Data Engineering to lead the enterprise data engineering strategy. This role involves designing and governing data platforms and pipelines that power analytics and business intelligence, while driving modernization of Kroll’s data ecosystem.

Responsibilities:

• Define and execute Kroll’s Data Engineering strategy and roadmap in alignment with the Data & AI vision. Translate enterprise goals into measurable data platform capabilities
• Champion modernization of data engineering practices including automation, observability, and scalability
• Partner with peers in Reporting, Analytics, AI, Technology, Infosec, corporate functions, and business units to deliver a unified and extensible data foundation
• Lead the design and operation of the enterprise data platform architecture across ingestion, transformation, and serving layers
• Define and enforce standards for data security, lineage, metadata, and quality
• Collaborate to operationalize compliance, privacy, and risk management
• Drive adoption of modern architectural patterns such as data mesh, lakehouse, and event-driven pipelines
• Oversee end-to-end data delivery: ingestion, transformation, orchestration, and consumption pipelines
• Implement CI/CD, GitOps, and Infrastructure-as-Code to streamline data deployment
• Ensure data systems meet reliability and performance SLAs through monitoring and proactive capacity planning
• Collaborate with product and analytics leads to enable reusable, certified data products that fuel AI and insight
• Build and mentor a global team of senior data engineers, architects, and platform engineers
• Establish engineering excellence and cross-functional collaboration with analytics and product teams
• Promote a culture of technical rigor, transparency, and continuous learning

Requirements:

• 12+ years of progressive experience in data engineering or data architecture, including at least 5 years leading enterprise-scale teams
• Proven success implementing modern data ecosystems (cloud, lakehouse, streaming, data mesh, etc.)
• Strong proficiency in ETL/ELT frameworks, distributed computing, and orchestration tools
• Bachelor's or Master's degree in Computer Science, Engineering, or related field
• Excellent communication and stakeholder engagement skills
• Deep experience architecting and managing modern data ecosystems across Azure and Databricks, with working knowledge AWS
• Expertise in designing and governing Lakehouse and Medallion architectures to unify structured, semi-structured, and unstructured data at scale
• Hands-on understanding of data fabric, data mesh, and domain-oriented architecture models
• Strong command of cloud infrastructure fundamentals — compute, storage, networking, cost optimization, and security
• Proven ability to design, build, and oversee ETL/ELT pipelines and data services using Chainsys, Azure Data Factory, Airflow, Databricks, and Delta Lake
• Advanced proficiency in Python and the Spark ecosystem (PySpark, Spark SQL), with demonstrated capability to set and enforce best practices
• Skilled in object-oriented and functional programming, asynchronous processing, and hybrid batch/streaming architectures
• Deep knowledge of API-driven data integration, SDK development, and API lifecycle management
• Experience operationalizing metadata management, data cataloging, and lineage tracking using Azure Purview, or equivalent
• Skilled in defining and enforcing data quality, reliability, and compliance frameworks
• Hands-on knowledge of observability practices — monitoring, alerting, and incident response with Prometheus, Grafana, or Datadog, or equivalent
• Expertise in SQL/Spark query tuning, data pipeline optimization, and distributed system performance engineering
• Experienced in scaling fault-tolerant pipelines for petabyte-level workloads ensuring high availability
• Knowledge of containerization (Docker, Kubernetes) and applying CI/CD and DevOps pipelines to data workflows
• Familiarity with modern frameworks such as FastAPI, Pydantic, Polars, and Pandas
• Experience automating data profiling, lineage, and governance reporting
• Champion of infrastructure-as-code, GitOps, and continuous integration standards
• Partner with AI and analytics teams to operationalize data pipelines supporting ML model training and inference
• Deep understanding of how data engineering architecture impacts model performance and explainability
• Architectural Thinker: Designs with scalability and resilience in mind
• Execution Leader: Converts strategy into measurable operational outcomes
• Innovator: Actively leverages emerging technologies to drive value
• Collaborator: Bridges engineering, analytics, and business priorities seamlessly
• Mentor: Develops diverse technical talent and fosters engineering excellence