Quant Data Lead

LazardUnited States,
$200,000 - $240,000

About The Position

The Quantitative Equity group at Lazard Asset Management is undertaking a multi-year initiative to build a new, cloud-native quantitative data, research, and production ecosystem. This role will be central to designing and delivering that greenfield platform while also supporting and maintaining legacy data environments. The position combines hands-on engineering with architectural decision-making, team leadership, and close collaboration with quantitative researchers.

Requirements

  • Bachelor’s or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field.
  • 8–12+ years of experience in data engineering, data architecture, or quantitative data platforms.
  • 5+ years of experience using technologies such as Snowflake, Databricks, Azure services or the like.
  • Strong proficiency in data modeling.
  • Hands-on experience building and operating data pipelines.
  • Familiarity with market, fundamental, benchmark, and security-master datasets used in quantitative equity.
  • Solid grounding in data governance and lineage.
  • Ability to operate in a startup-style environment, balancing architecture, hands-on coding, and rapid iteration.

Responsibilities

  • Lead the design and evolution of an end-to-end quantitative data ecosystem, including ingestion, storage, modeling, access, and governance.
  • Develop high-quality pipelines and data products that support quantitative research, model development, production workflows, and analytics.
  • Define and implement data-modeling approaches suited to quantitative equity workflows.
  • Establish core data-governance practices across metadata, quality, lineage, and documentation.
  • Evaluate, onboard, and integrate datasets and vendors; design scalable processes for ingesting market, fundamental, and alternative data.
  • Contribute hands-on code, review designs, set engineering standards, and help recruit and mentor data engineers as the team grows.
  • Support existing production data systems during the build-out and migration to the new ecosystem.

Benefits

  • Comprehensive, competitive benefits
  • Highly individualized employee experience
  • Investment in career development
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