Data Engineering Lead

Head Of Data Engineering

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 perspectives allow creative ideas and original thinking to drive our business forward.

Lazard Asset Management is seeking a Head of Data Engineering to own and lead a full-scale modernization of the LAM data domain platform, tooling, architecture, and practices from the ground up. This is a rare mandate: the authority to help rebuild a financial services data estate into a modern, governed, cloud-native foundation that delivers data as a service to the business and as a first-class input to AI and machine learning.

The ideal candidate has served as a head of data engineering or head of a data office or equivalent senior data platform leader at an asset management firm or division with direct experience managing enterprise-scale data estates across multiple business lines. You bring both the architectural authority to design what needs to be built and the technical hands-on depth to be credible with the engineers doing the building. You have modernized complex legacy data environments before and know how to do it without breaking production.

What You'll Do:

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 Enablement

• 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
• Establish and enforce engineering best practices: code review, testing standards, CI/CD for data pipelines, infrastructure-as-code, and documentation
• 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

What You'll Need:

• 15+ years of experience in data engineering, data platform engineering, or data architecture, with at least 5 years in a senior leadership role
• Proven track record leading data platform modernization at a financial services firm, asset manager, investment bank, broker-dealer, or similarly regulated institution with direct experience across multiple business lines or data domains
• Demonstrated experience designing and building enterprise-scale, cloud-native data platforms from the ground up or through major re-architecture, including full ownership of technology decisions from ingestion through serving
• Deep hands-on technical expertise: you can design the architecture, review the code, debug the pipeline, and evaluate the tooling, not just manage teams that do it
• Strong proficiency in Python, SQL, and Spark-based processing (PySpark); ability to write and review production-quality code
• Expert-level experience with cloud data platforms (Azure, AWS, or GCP) and modern data stack patterns: data lakehouses, medallion architecture, streaming vs. batch tradeoffs, and cost/performance optimization
• Experience with enterprise data orchestration and transformation tooling: Prefect, Airflow, dbt, Spark, or equivalent; hands-on selection and configuration experience, not just oversight
• Proven ability to design and enforce enterprise security controls for data platforms: RBAC/ABAC, data classification, encryption, masking, and integration with enterprise identity and access management
• Direct experience managing large-scale legacy data migrations to cloud-native architectures, including migration program design, risk management, and production-safe cutover strategies
• Experience operating in a regulated financial services environment, including familiarity with data-related regulatory obligations (SEC, FINRA, GDPR, CCPA, records retention) and audit support as a control owner
• Undergraduate degree or higher in Computer Science, Engineering, Data Science, Mathematics, or a related technical field

It's a bonus to have:

• Experience building data platforms that serve as inputs to AI and ML systems, including training data pipelines, feature stores, model data lineage, and inference data serving
• Familiarity with asset management data domains: portfolio data, market data, reference data, client/distribution data, performance and risk data
• Experience with data mesh or domain-oriented data ownership architectures in a multi-LOB financial services context
• Background with API-driven data access layers (e.g., Azure API Management, Kong, or equivalent) and data product catalog tooling (Collibra, Atlan, Alation, or similar)
• Experience with infrastructure-as-code and DataOps practices: Terraform, CI/CD for pipelines, automated data quality testing, and SRE principles applied to data systems
• Relevant certifications: cloud architect certifications (AWS Solutions Architect, Azure Data Engineer, GCP Professional Data Engineer), CDMP, or equivalent

What We Offer

We strive to enhance the total health and well-being of our employees through comprehensive, competitive benefits. Our goal is to offer highly individualized employee experience that enables you to balance your commitments to career, family, and community. When you work for Lazard, you are working for an organization that