Data Engineering Lead

Lazard • New York, NY

3d • $200,000 - $280,000 • Hybrid

About The Position

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.

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• 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
• 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
• 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
• 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
• 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

Benefits

• Comprehensive, competitive benefits
• Highly individualized employee experience that enables you to balance your commitments to career, family, and community
• Organization that cares about your unique talents and passions and will continue to invest in the development of your career

What This Job Offers

• Job Type: Full-time
• Career Level: Senior