Head of Data Engineering

Lazard•New York, NY

3d•$200,000 - $250,000

About The Position

Lazard Asset Management (LAM) is seeking a Head of Data Engineering to lead the ground-up modernization of its data landscape. This is a rare build mandate: replace a fragmented legacy estate with a governed, cloud-native data service platform that turns data into a reusable, on-demand asset and accelerates the firm’s ability to adopt AI, empower self-service, and integrate new capabilities through a plug-and-play architecture. Working hand in hand with business and shared service teams and the central AI and Data team, you will define and deliver a new end-to-end data strategy, from business needs through to shared infrastructure. You are a senior data platform leader from asset management or a comparable regulated financial institution who has modernized complex legacy environments without disrupting production. You pair the architectural vision to set direction with the hands-on depth to earn credibility with engineers.

Requirements

15+ years in data engineering, platform engineering, or data architecture, including 5+ years in senior leadership
Proven record modernizing legacy data estates (e.g., Oracle, Sybase, PostgreSQL) across multiple business lines at an asset manager, investment bank, or similarly regulated financial institution
Experience building enterprise-scale, cloud-native data platforms (Azure, AWS, or GCP) with full ownership of technology decisions, and deep expertise in modern architecture (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 (pipeline automation, observability, automated data quality testing, infrastructure-as-code such as Terraform)
Track record delivering data foundations for AI/ML and governed self-service analytics
Strong command of data security, governance, and regulatory obligations (SEC, FINRA, GDPR, CCPA, records retention)
Exceptional communicator who has partnered with business, shared service, and central AI and data teams to execute enterprise data strategy and translate technical vision into business outcomes
Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related field

Nice To Haves

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

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