Senior Manager, Data & AI Architect (Remote)

RTX•US-CT-REMOTE, CT

1d•$132,400 - $251,600•Remote

About The Position

Our Pratt & Whitney Digital Technology team has an exciting new remote job opportunity for a Senior Manager, Data & AI Architect. We are seeking an experienced Data & AI Architect to design, develop, and govern scalable, secure, and high-performing data architectures that support business intelligence, analytics, artificial intelligence, and operational needs. The Data Architect will work closely with business stakeholders, data engineers, application teams, security teams, and leadership to establish data strategies, standards, models, and platforms that enable reliable and accessible data across the organization. The ideal candidate has hands-on experience designing Databricks-based Lakehouse architectures, a strong understanding of the Medallion Architecture, including Bronze, Silver, and Gold data layers and experience architecting data solutions for SAP ECC to SAP S/4HANA migrations.

Requirements

Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related field with 10+ years of applicable work experience; OR an Advanced Degree with 7+ years of applicable work experience.
7+ years of experience in data architecture, data engineering, database architecture, or a related discipline.
Strong experience designing and implementing modern data architectures and Lakehouse solutions.
Hands-on experience with Databricks and the Databricks Data Intelligence Platform.
Strong understanding of Medallion Architecture, including Bronze, Silver, and Gold data layers.
Strong knowledge of Apache Spark, PySpark, and SQL.
Experience designing data warehouses, data lakes, and/or Lakehouse architectures.
Strong knowledge of data modeling, including dimensional, relational, and analytical data models.
Experience designing ETL/ELT pipelines and data integration solutions.
Experience with at least one major cloud platform such as AWS, Microsoft Azure, or Google Cloud.
Understanding of data governance, data quality, metadata, data lineage, security, and privacy.
Demonstrated ability to design and implement enterprise‑scale data and AI/ML architecture solutions.
Strong communication and stakeholder-management skills.

Nice To Haves

Experience with Delta Lake and Delta Live Tables/Lakeflow Declarative Pipelines.
Experience with Unity Catalog for data governance, access control, discovery, and lineage.
Experience with Databricks, Delta Lake, Matillion, HVR, or other modern data engineering platforms.
Deep knowledge of SAP modules (MM, SD, FI/CO, PP, PM) and related master data structures.
Experience supporting large‑scale SAP modernization or digital core transformation programs.
Experience with batch and real-time/streaming data architectures.
Knowledge of data mesh, data fabric, domain-driven data architecture, and event-driven architectures.
Experience with cloud services supporting data ingestion, storage, processing, and analytics.
Experience designing data platforms for AI/ML and Generative AI workloads.
Familiarity with data catalog, data quality, data observability, and master data management solutions.
Databricks, cloud, or data architecture certifications are a plus.

Responsibilities

Design and implement scalable enterprise data architectures using Databricks and modern Lakehouse technologies.
Define and implement Medallion Architecture patterns, including Bronze Layer, Silver Layer and Gold Layer.
Architect and implement semantic layers that enable consistent enterprise metrics, business definitions, and analytics across heterogeneous systems.
Design data models that blend ECC and S/4HANA structures into unified, analytics‑ready data products.
Partner with SAP functional experts, data engineers, and business stakeholders to define data requirements, lineage, and transformation logic.
Develop conceptual, logical, and physical data models aligned with business and technical requirements.
Design scalable data lake, Lakehouse, and data warehouse architectures.
Establish data architecture standards, principles, patterns, and best practices.
Ensure alignment with enterprise governance, VAULTIS principles, and data quality standards.
Design data ingestion and integration strategies for batch and streaming workloads.
Partner with data engineers to develop efficient ETL/ELT pipelines using Databricks, Apache Spark, and SQL.
Define approaches for data quality, lineage, metadata management, governance, and observability across the data platform.
Design architectures that support analytics, business intelligence, machine learning, and generative AI use cases.
Develop data migration and modernization strategies for legacy platforms.
Ensure data architectures meet security, privacy, compliance, scalability, availability, and performance requirements.
Collaborate with enterprise architects, application teams, data engineers, analysts, data scientists, and business stakeholders.
Create architecture documentation, data-flow diagrams, reference architectures, and technical standards.
Identify opportunities to improve data platform performance, reliability, scalability, and cost efficiency.
Provide technical leadership and mentorship to data engineering and analytics teams.
Collaborate with AI/ML teams to prepare, model, and structure SAP data for intelligent applications, including feature engineering and model‑ready datasets.