Carrier Technologies - Platform Architect - Data Engineering

Description :

About the role :

The Platform Architect is responsible for end-to-end architecture, technical direction, and ownership of the enterprise data platform built on a Lakehouse architecture, primarily deployed across AWS and GCP. This role defines how the platform is designed, built, governed, and evolved, ensuring it remains scalable, secure, interoperable, and cloud-agnostic through the use of open data standards.

You will own platform development strategy and architectural decisions, establish reference architectures and guardrails, and partner with platform engineering, data engineering, governance, security, and product teams to enable analytics, AI/ML, and data products at enterprise scale while minimizing vendor lock-in. This is a hands-on architecture leadership role with accountability for the technical integrity, long-term sustainability, and cross-cloud consistency of the data platform.

Key Responsibilities :

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, tradeoffs, 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 :

1. Cloud-native storage and processing services

2. Environment isolation (dev / test / prod)

3. Networking and identity models

4. 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 (added advantage).

- 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.