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Lazard

Advises on corporate deals and manages investments

Head of Data Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeNew York, NY, USAIn-person

About the Job

Requirements

  • 15+ years in data engineering, platform engineering, or data architecture, including 5+ years in senior leadership.
  • A proven record modernizing legacy data estates, such as Oracle, Sybase, or PostgreSQL, across multiple business lines at an asset manager, investment bank, or similarly regulated financial institution.
  • Experience building enterprise-scale, cloud-native data platforms on Azure, AWS, or GCP, with full ownership of technology decisions and deep expertise in modern architecture, including lakehouse and medallion design, streaming and batch, data products, APIs, and platforms such as Snowflake, Databricks, and Collibra.
  • Hands-on credibility in Python, SQL, and enterprise orchestration and transformation tooling, with experience running a DataOps function, including pipeline automation, observability, automated data quality testing, and infrastructure-as-code such as Terraform.
  • A track record delivering data foundations for AI/ML and governed self-service analytics.
  • Strong command of data security, governance, and regulatory obligations, including SEC, FINRA, GDPR, CCPA, and records retention.
  • A bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related field.

Responsibilities

  • 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 using phased cutovers, parallel runs, automated reconciliation, and rollback plans, and report progress, value, and risk to technology and business leadership.
  • Define the target architecture for ingestion, lakehouse storage, transformation, serving, and 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, including ingestion, reconciliation, data quality, distribution, and reporting, with orchestration, monitoring, alerting, SLAs, and runbooks.
  • Track and improve operational KPIs for timeliness, accuracy, and availability, and report performance to business stakeholders.
  • Make AI-ready data a platform default by providing 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, including RBAC/ABAC, classification, encryption, masking, lineage, and 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 through CI/CD, infrastructure-as-code, automated testing, observability, and incident management.

Desired Qualifications

  • Asset management data domain expertise in portfolio, market, reference, client, performance, and risk data.
  • Experience with data mesh or domain-oriented ownership, and with catalog and API tooling such as Collibra, Atlan, and Azure API Management.
  • Exposure to GenAI data patterns such as feature stores, vector databases, and LLM data pipelines.
  • Relevant certifications, such as Azure, AWS, or GCP data/architect certifications, or CDMP.

About the Company

Lazard

Advises on corporate deals and manages investments

Lazard advises companies and governments on major financial decisions and manages investment portfolios for institutions, families, and wealthy individuals. Its financial advisory teams work on mergers, capital raising, and debt restructurings, combining industry knowledge with local teams that coordinate across markets. The asset management business researches investments and runs portfolios across stocks, bonds, and alternative assets, while specialized advisers analyze geopolitical risks for corporate leaders. Lazard earns fees for advisory assignments and investment management, with investment fees generally tied to client assets. The firm traces its roots to 1848 and is headquartered in New York City.

Company size
1,001-5,000
Company stage
IPO
Headquarters
New York, NY
Founded
1848

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