Head of Data Engineering at Lazard in New York, NY

Job description

Lazard is one of the world’s preeminent financial advisory and asset management firms. Our people and culture make the difference. While global in presence and reach, ours is a close, collaborative community of just over 3,000 professionals. Lazard is a place of continuous knowledge sharing, skill development and relationship building, where professionals grow and succeed together. Our entrepreneurial culture, flat structure and embrace of individual differences, allow creative ideas, original concepts, and unique perspectives to drive our business forward — and for careers to take flight. Lazard Asset Management (LAM) is seeking a Head of Data Engineering to lead the ground-up modernization of its data landscape. This is a rare build mandate: replace a fragmented legacy estate with a governed, cloud-native data service platform that turns data into a reusable, on-demand asset and accelerates the firm’s ability to adopt AI, empower self-service, and integrate new capabilities through a plug-and-play architecture. Working hand in hand with business and shared service teams and the central AI and Data team, you will define and deliver a new end-to-end data strategy, from business needs through to shared infrastructure. You are a senior data platform leader from asset management or a comparable regulated financial institution who has modernized complex legacy environments without disrupting production. You pair the architectural vision to set direction with the hands-on depth to earn credibility with engineers. We'll trust you to: Define and own LAM's end-to-end data strategy, from business priorities and use cases through data products, platforms, and shared infrastructure, co-designing solutions with investment, distribution, and operations teams to deliver measurable outcomes Partner with the central AI and Data team to reuse firmwide platforms, models, and data products, adopt enterprise data standards, and integrate cleanly into the broader Lazard data ecosystem Own the multi-year roadmap to retire legacy databases, on-premises infrastructure, and fragile ETL, rebuilding LAM's data estate on a modern cloud-native foundation Execute production-safe migrations (phased cutovers, parallel runs, automated reconciliation, rollback plans) and report progress, value, and risk to technology and business leadership Define the target architecture (ingestion, lakehouse storage, transformation, serving, observability) and build a data service platform with well-defined data products, published SLAs, and API-first consumption Design a modular, plug-and-play architecture on open standards so tools, vendors, and data sources can be swapped quickly and at low cost; set canonical data models, metadata standards, and data contracts, and own the data engineering toolchains Stand up and run a new DataOps function that automates critical processes (ingestion, reconciliation, data quality, distribution, reporting) with orchestration, monitoring, alerting, SLAs, and runbooks Track and improve operational KPIs (timeliness, accuracy, availability) and report performance to business stakeholders Make AI-ready data a platform default (trusted, documented, lineage-tracked datasets for training, backtesting, retrieval, and inference) and enable governed self-service via a data catalog, semantic layer, and discoverable data products, replacing shadow pipelines and one-off extracts Embed security and governance by design (RBAC/ABAC, classification, encryption, masking, lineage, data quality SLOs); meet financial services regulatory requirements and act as control owner for audits and exams, partnering with InfoSec, Risk, and Compliance Build, lead, and mentor a high-performing data engineering team, instilling production-first discipline: CI/CD, infrastructure-as-code, automated testing, observability, and incident management You'll need to have: 15+ years in data engineering, platform engineering, or data architecture, including 5+ years in senior leaders