Data Platform Architect

Job Title: Data Platform  Architect

Location : Pittsburgh PA

Duration : Full time Role

Role Overview

We are seeking a Data Platform Architect to lead the technical and functional architecture of multiple enterprise data products within a centralized data product organization. This is a senior, hands-on architectural role responsible for shaping how data is ingested, modeled, governed, and consumed across banking domains.

The role requires strong depth across data platforms, streaming, analytics, and governance, along with the ability to translate core banking processes into scalable and reusable data products. You will work across multiple teams, guiding architecture while ensuring alignment with enterprise standards and regulatory requirements.

Key Responsibilities

Data Product & Domain Architecture

• Define end-to-end architecture for multiple data products, from source systems to analytics and downstream consumption.
• Design and govern logical, physical, and semantic data models, including facts, dimensions, metrics, and hierarchies.
• Apply domain-driven and data product–oriented design principles to ensure reusability and consistency.
• Establish clear data contracts and interfaces between domains and platforms.

Platform & Technical Architecture

• Architect solutions across Hadoop-based platforms, modern data lakehouse architectures, and streaming systems.
• Define patterns for batch, near real-time, and event-driven processing using Spark, Spark Streaming, and Kafka.
• Guide integration across on-prem and cloud environments, including hybrid architectures.
• Ensure architectural alignment with enterprise data platforms and BI ecosystems.

Application, Streaming & Integration Design

• Design data services and ingestion frameworks using Java and Spring Boot.
• Leverage Python for data engineering, orchestration, and analytical workloads.
• Architect Kafka-based pipelines for ingestion, decoupling, and event-driven data products.
• Apply graph database patterns where relationship-centric modeling is required.

Analytics & Semantic Layer Enablement

• Define enterprise-grade semantic models to support BI tools such as Power BI and Fabric.
• Ensure consistent business definitions across reports, dashboards, and analytics products.
• Enable one-to-many consumption models where a single data product supports multiple use cases.

Observability, Quality & Operations

• Embed data observability into architecture, including monitoring, alerting, and performance metrics.
• Use ELK stack (Elasticsearch, Logstash, Kibana) for logging, diagnostics, and operational insights.
• Design for resiliency, scalability, and operational excellence in data platforms.

Governance, Security & Compliance

• Integrate data governance, lineage, metadata, and data quality controls into all architectures.
• Ensure compliance with security, privacy, and regulatory standards typical of large financial institutions.
• Define ownership, stewardship, and lifecycle management for data products.

Collaboration & Leadership

• Partner closely with Data Product Managers, Tech Leads, Data Engineers, and Platform teams.
• Review and approve solution designs, models, and implementations.
• Act as a bridge between enterprise architecture standards and delivery execution.
• Influence architectural decisions across multiple initiatives and data products.

Required Qualifications

• 8 years of experience in data architecture, data engineering, or analytics architecture roles.
• Strong hands-on expertise in data modeling, lakehouse platforms, and streaming architectures.
• Proven experience designing and governing enterprise-scale data products.
• Strong communication skills with both technical and business stakeholders.
• Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s preferred).

Skills & Technical Expertise

• Data Platforms & Processing
• Hadoop ecosystems, modern data lakehouse architectures
• Apache Spark and Spark Streaming
• Kafka for real-time and event-driven data pipelines
• Programming & Integration
• Java, Spring Boot for data services and ingestion frameworks
• Python for data engineering and analytics
• Integration patterns across hybrid (on-prem cloud) environments
• Modeling & Analytics
• Dimensional, canonical, and domain-driven data modeling
• Semantic layer and BI architecture (Power BI, Fabric, Tableau)
• Graph database modeling for relationship-intensive use cases
• Observability & Operations
• Data observability, monitoring, and platform resilience
• ELK stack for logging, metrics, and troubleshooting
• Governance & Security
• Enterprise data governance, lineage, metadata, and quality
• Security, access control, and compliance in regulated environments

Preferred Qualifications

• Experience in banking or financial services, especially core banking domains (deposits, loans, transactions).
• Familiarity with data mesh or domain-based data product operating models.
• Experience supporting BI modernization initiatives
• Exposure to cloud platforms (Azure, AWS, Google Cloud Platform) in regulated enterprise setups.