Job description

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 differences, allow creative ideas, original concepts, and unique perspectives to drive our business forward — and for careers to take flight. 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. We'll trust you to: • 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 You'll need to have: • 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 It's a bonus to have: • 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) 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 a 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 - $250,000 USD. Various factors contribute to determining the actual base compensation offered, including but not limited to the applicant’s years of relevant experience, career tenure, qualifications, level of education attained, certifications or other professional licenses held, 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. Does this sound like you? Apply! We’ll get in touch on the next steps. Representation at Lazard Lazard is an intellectual capital business committed to delivering the best advice and solutions to clients. To achieve these objectives, we focus on attracting, developing and retaining the best talent. 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. Lead the modernization of LAM's legacy data estate into a governed, cloud-native platform built for AI and self-service analytics. Requires 15+ years in data engineering at an asset manager, bank, or similar regulated firm.

Requirements

Must-haves

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
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
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
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
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
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)

Nice-to-haves

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Application prep

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Keywords to mirror

aiapici/cdazureawsgcppythonsqlmlllm

Platform: oracle-cloud
Effort: medium

Benefits

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Vision