Manager, AI & Data Engineering

1. Platform Architecture & Technical Ownership

• Own the overall architecture of the enterprise data platform based on a Lakehouse paradigm, covering storage, compute, metadata, governance, and observability.

• Define and maintain reference architectures, design patterns, and architectural standards applicable across AWS and GCP.

• Act as the final technical authority for platform‑level architectural decisions, trade‑offs, and exceptions.

• Ensure architectural choices support scalability, resilience, security, cost efficiency, and operational excellence.

2. Lakehouse & Open‑Standards Strategy

• Design the platform around open data standards to ensure portability and long‑term flexibility.

• Standardize on open table formats (e.g., Apache Iceberg or equivalent) and open file formats (e.g., Parquet) as the foundation of the Lakehouse.

• Enforce separation of storage and compute, enabling multiple analytics and processing engines without vendor lock‑in.

• Define principles for schema evolution, ACID guarantees, time travel, and multi‑engine interoperability.

3. Multi‑Cloud Platform Design (AWS & GCP)

• Architect the Lakehouse platform to run on AWS and GCP, with cloud‑specific implementations mapped to a common logical architecture.

• Define standards and patterns for:

• Cloud‑native storage and processing services

• Environment isolation (dev / test / prod)

• Networking and identity models

• Security, encryption, and access controls

• Establish cloud‑agnostic abstractions and guardrails so data products behave consistently across clouds.

• Ensure platform design aligns with enterprise security, compliance, and cost‑management requirements.

4. Platform Development & Engineering Enablement

• Partner with Platform Engineers and Data Engineers to translate architecture into reusable platform capabilities, frameworks, and templates.

• Define configuration‑driven and template‑based approaches to accelerate onboarding and reduce bespoke development.

• Review and guide detailed solution designs to ensure alignment with architectural standards.

• Enable self‑service platform usage while preserving governance and operational controls.

5. Governance, Metadata & Lineage Architecture

• Define the governance architecture for the Lakehouse, including metadata management, lineage, access control, and auditability.

• Ensure support for open metadata and lineage standards (e.g., OpenLineage‑style event models).

• Embed data quality, ownership, discoverability, and compliance as first‑class architectural concerns.

• Align platform design with enterprise data governance and regulatory expectations.

6. Operational Excellence & Non‑Functional Architecture

• Architect for high availability, observability, and operational simplicity.

• Define platform‑level SLAs, SLOs, and operational readiness criteria.

• Incorporate FinOps principles into architectural decisions to optimize performance and cost.

• Ensure the platform supports Day‑2 operations, incident management, and continuous improvement.

7. Stakeholder Leadership & Architecture Governance

• Serve as the primary architecture interface between engineering teams, security, governance, and business stakeholders.

• Clearly communicate architectural vision and decisions through architecture documents, standards, and review forums.

• Lead architecture reviews, roadmap planning, and technology evaluations.

• Mentor senior engineers and architects, raising overall platform and architecture maturity.

Required Qualifications

• 16+ years of experience in data engineering, platform engineering, or data architecture roles.

• Proven experience architecting enterprise‑scale data platforms.

• Strong experience designing and governing Lakehouse architectures.

• Hands‑on architectural experience with AWS and/or GCP, with the ability to design for both.

• Deep understanding of open data standards, including open table formats, file formats, metadata, and lineage.

• Experience defining reference architectures, guardrails, and engineering standards.

• Strong ability to balance long‑term architectural vision with near‑term delivery needs.

Preferred / Optional Qualifications

• Experience with Microsoft Azure data and analytics services (optional, not required).

• Experience designing multi‑cloud or cloud‑agnostic data platforms.

• Familiarity with multiple analytics and query engines across cloud ecosystems.

• Experience with enterprise data governance frameworks and metadata platforms.

• Exposure to AI/ML platform integration on Lakehouse architectures.

• Prior experience leading large‑scale platform modernization or migration initiatives
  

  Benefits

  We offer a competitive total rewards package that may include other benefits andwell‑beingprograms. Offerings vary by role and location and are designed to support employees’ health, security, and success.

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  If you require a reasonable accommodation to complete the application process,participatein an interview, or otherwise engage in the hiring process, please contact us at [email protected].We will make every effort to meet your needsin accordance withapplicable laws.

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