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# Senior Data Architect

- **Company:** EPAM Systems, Inc.
- **Location:** Newtown, PA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $170,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Batch Processing, BigQuery, Cloud Database, Cloud Storage, Code Review, Computer Programming, Databases, Data as a Services, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Mart, Identity and Access Management, Python (Programming Language), Metadata, Performance Tuning, Query Optimization, SQL Databases, Enterprise Data Management, Google Cloud, Data Classification, Sql Optimization, Multi-Cloud, Backend, Data Layers, Semi-structured Data, Physical Data Models, Restful APIs, Model Context Protocol, Data Pipelines, Api Management
- **Published:** October 15, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3448866393&tx=JL7973FFJ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

If you are passionate about multi-cloud cost, usage, resource, and financial datasets, and love architecting clean, scalable analytical engines in Google Cloud (GCP) / BigQuery, we want to hear from you., * Experience: Strong professional background in data engineering, data architecture, analytics engineering, or backend/data platform development

 * Data Modeling: Deep expertise in conceptual, logical, physical, dimensional, canonical, and domain-oriented data modeling
 * Cloud Data Warehousing: Advanced SQL skills and hands-on experience designing large-scale analytical datasets, data marts, and multi-source integration pipelines, specifically using Google Cloud BigQuery (or similar cloud data warehouses)
 * GCP Ecosystem: Strong GCP knowledge, including BigQuery, Cloud Storage, IAM, service accounts, Pub/Sub, and GKE integration patterns
 * Mapping & Specifications: Proven ability to produce source-to-target mappings, transformation specifications, data contracts, and lineage documentation
 * Performance Tuning: Expertise in BigQuery partitioning, clustering, performance tuning, and cost-efficient query design
 * Programming & Integration: Hands-on Python experience for data services, Proofs of Concept (POCs), transformations, or API integrations. Experience with REST APIs, event-driven integration, batch processing, and semi-structured data
 * Governance & Security: Strong understanding of data governance, data quality, reconciliation, schema evolution, and least-privilege access patterns
 * Collaboration & Review: Experience conducting architecture reviews, mentoring engineers, and driving consensus across cross-functional technical teams

## Description

* Data Architecture & Modeling: Define the target data architecture for multi-cloud cost, usage, resource, contract, financial, metadata, and operational datasets. Design conceptual, logical, and physical data models optimized for BigQuery analytical workloads
 * Documentation & Governance: Create source-to-target mappings, transformation rules, data contracts, lineage documentation, and business definitions. Define clear ownership boundaries across raw, standardized, curated, and analytics-ready data layers
 * Semantic & Domain Modeling: Establish canonical entities, domain models, semantic datasets, and reusable dimensions for traditional reporting as well as modern AI-agent use cases
 * Integration & Ingestion Design: Design integration patterns for APIs, files, databases, event streams, and semi-structured enterprise data. Ensure data is properly mapped, normalized, and reconciled
 * AI & Agent Readiness: Partner with AI Engineers to design safe, discoverable, read-only BigQuery datasets and MCP tools for agent access. Define query and schema standards that enable reliable agent reasoning, structured responses, analytics, and recommendations
 * Performance & Cost Optimization: Review SQL queries, data pipelines, schemas, and integration designs for quality, scalability, maintainability, performance, and cost efficiency. Define BigQuery best practices for partitioning, clustering, materialized views, and query optimization
 * Data Governance & Leadership: Establish governance standards for lineage, ownership, access control, retention, data classification, quality, and freshness. Participate in architecture and code reviews, offering technical leadership across the data domain