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
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
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 cares about your unique talents and passions and will continue to invest in the development of your career.
We expect the base salary range for this role to be approximately $200,000–$280,000 USD. Various factors contribute to determining the actual base compensation offered, including but not limited to years of relevant experience, career tenure, qualifications, level of education attained, certifications, and relevant skills for the role. Base salary is one component of Lazard’s compensation package, which also includes comprehensive benefits and may include incentive compensation.
Representation at Lazard
Lazard is an intellectual capital business committed to delivering the best advice and solutions to clients. We believe that a workforce comprised of people who represent a wide array of backgrounds, experiences, and perspectives creates a rich variety of thought that empowers us to challenge conventional wisdom, solve problems creatively, and make better decisions.
Lazard was built on the premise that a multicultural firm can best serve a global clientele. As a global firm that has grown organically from local roots in different countries, we have a deep tradition of respecting and appreciating individual differences. Doing so has been core to our success for over 175 years. We are committed to sustaining an environment where every colleague is supported in their professional pursuits, can maximize their individual potential, and contribute to our collective success.