Data Engineering Lead
Platform Vision & Technical Architecture
Define and own the end-to-end technical architecture of the LAM data platform including ingestion, storage, transformation, serving, and observability layers across investment, distribution, sales, marketing, and operational functions.
Establish the LAM data platform as a governed, cloud-native data-as-a-service capability: self-serve access patterns, well-defined data products, published SLAs, and consumption APIs for business users, analysts, and AI/ML systems.
Drive architectural decisions on data platform technology, cloud provider strategy, compute and storage patterns, data lakehouse design, medallion architecture, data mesh or domain-oriented ownership models with hands-on involvement in design reviews and proof-of-concept builds.
Define and enforce canonical data models, metadata standards, and data product contracts across LAM’s data estate.
Evaluate, select, and own the LAM data engineering toolchain: orchestration, transformation, catalog, quality, observability, and CI/CD for data pipelines.
Legacy Migration & Platform Modernization
Own and lead the multi-year program to migrate LAM’s legacy data systems including legacy databases, on-premises infrastructure, fragile ETL pipelines, and ad-hoc data flows onto a modern, cloud-based, governed architecture.
Design migration strategies that protect production stability throughout the transition: phased cutover, parallel runs, data validation frameworks, and rollback plans.
Establish engineering patterns, reusable frameworks, and platform services that enable migration teams to move at speed without sacrificing reliability or governance.
Deliver measurable milestones against a credible multi-year modernization roadmap, reporting progress to technology and business leadership.
Enterprise Controls & Security
Design and enforce enterprise-grade data security controls across the platform: role-based and attribute-based access control, data classification and sensitivity labeling, encryption at rest and in transit, data masking and tokenization for sensitive datasets.
Ensure the platform meets all applicable financial services regulatory and compliance requirements, including data residency, records retention, auditability, and supervisory data access obligations.
Own the platform’s operational control posture: define and enforce SLAs, incident response runbooks, data quality SLOs, and lineage requirements that satisfy both business and regulatory stakeholders.
Partner with Information Security, Risk, and Compliance to integrate the data platform into the firm’s broader enterprise security and technology risk frameworks.
Support internal audit, regulatory examinations, and external reviews with auditable evidence of platform controls, data lineage, and access governance.
Data as a Service & AI
Architect and deliver data services and APIs that enable self-serve analytics, application development, and AI/ML workflows across LAM, building the data layer that powers Lazard’s AI strategy.
Partner with data science and AI teams to ensure the platform delivers clean, well-documented, lineage-tracked datasets as first-class inputs to model training, backtesting, and inference pipelines.
Define data product standards: ownership, documentation, freshness SLAs, quality contracts, and consumption interfaces for both human and machine consumers.
Drive adoption of the platform as a shared enterprise service, displacing one-off data pulls, shadow pipelines, and siloed data stores across LAM.
Engineering Excellence & Team Leadership
Build, lead, and mentor a high-performing data engineering organization; hire for technical depth and engineering rigor and develop talent at every level.
Create a culture of production-first engineering: observability, alerting, on-call discipline, postmortems, and continuous improvement.
Partner with LDAG on firmwide data standards, architecture alignment, and AI/data science enablement to ensure LAM’s platform integrates cleanly into the broader Lazard data ecosystem.