Principal Data Architect

Method, a GlobalLogic company•New York, NY

About The Position

We are seeking an experienced Principal Data Architect to join our Data & AI team. In this leadership role, you will help shape modern data and AI architecture strategies that enable organizations to achieve business outcomes through scalable, reliable, and secure data platforms. You will work closely with senior stakeholders, client teams, and engineering teams to define architectural direction, guide implementation approaches, and support the delivery of data-driven capabilities across analytics, AI, and operational use cases. You will exemplify Method culture and values by driving standards of excellence and mentoring and educating those who are accountable to you on projects or within the data engineering discipline. You will be reporting into the head of the Data & AI team. This role requires a strong blend of technical depth, architectural thinking, client engagement and team mentorship. You will be expected to navigate ambiguity, align business and technology priorities, and help teams deliver practical, high-quality solutions that support long-term value creation. In addition to shaping solution direction, you will play an important role in guiding and developing data engineering teams through coaching, knowledge sharing, and hands-on support. Travel for team, client and sales meetings is required, typically up to 25%. This is a US-based position in the East Coast or Mid-Central time zone.

Requirements

8+ years of experience in data engineering, with 3+ years in a technical leadership role.
Experience designing and implementing modern data architectures, including lakehouse, data mesh, and real-time data platforms, and evaluating technology trade-offs.
Strong background in distributed systems and data-intensive applications.
Experience building and operating large-scale data platforms, including data warehouses, streaming systems, and data lakes.
Expertise in data modeling, data platform operations, observability, and reliability engineering for data systems.
Proficiency with modern data and distributed technologies such as Spark, Kafka, Flink, Trino/Presto, Iceberg/Delta/Hudi, and distributed storage systems.
Experience designing streaming and low-latency architectures and understanding their operational trade-offs.
Experience designing and deploying AI/ML platforms, including feature stores, model training pipelines, and model serving systems.
Familiarity with MLOps and ML productionization practices, including CI/CD, model monitoring, and governance.
Strong experience working with cloud platforms (AWS, Azure, or GCP) and cloud-native data services.
Experience with modern data platforms such as Snowflake, Databricks, or similar analytics environments.
Understanding of data governance frameworks, data cataloging, lineage, and privacy controls.
Experience implementing data-driven decision systems, advanced analytics, and machine learning solutions in enterprise environments.
Advise C-suite executives on data strategy, AI investment, and digital transformation at the highest organizational level.
Strong experience supporting pre-sales, solution shaping, and technical discovery engagements.
Support client partners in growing key accounts by identifying new opportunities, navigating complex situations, and educating clients on the value of our work beyond data and AI.
Excellent stakeholder management skills and the ability to build trust with both business and technical leaders.
Ability to cultivate strong partnerships across organizations to drive adoption of modern data and AI capabilities.
Strong influencing skills, with the ability to galvanize, inspire, and align others around a shared direction.
Contribute Thought Leadership through presentations, blogs, or public talks
Strong conflict resolution and risk management skills.
Team player with the capacity to mentor, guide, and champion engineering teams and architects.
Commitment to technical excellence, pragmatic delivery, and continuous improvement.

Responsibilities

Lead complex data and AI platform initiatives, addressing architectural concerns while enabling delivery teams to meet defined standards, timelines, and budgets.
Provide client-facing technical leadership on data architecture, data engineering, analytics platforms, and AI/ML infrastructure.
Collaborate with enterprise architects and senior technology stakeholders to align on architectural decisions, trade-offs, and implementation approaches.
Communicate architectural concepts clearly to both engineering teams and business stakeholders.
Design architectures that bridge operational systems and analytical platforms, enabling data products, advanced analytics, and AI capabilities.
Define and implement architectures for data governance, security, privacy, and regulatory compliance.
Establish and integrate data quality frameworks, observability practices, and data reliability processes.
Partner with sales and pre-sales teams to shape solution approaches, validate technical feasibility, and support proposals.
Develop new value propositions and offerings for the data engineering at Method, create go to market strategies, test and refine the offerings during the sales pursuits.
Grow and maintain the data engineering team. Provide career guidance and facilitate career development opportunities across Method.
Create an environment that brings together the best of various data disciplines with care for the uniqueness of each project.
Foster a culture of curiosity and learning, creating opportunities for employees to learn and grow into our future leaders.