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Senior Data Architect
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
What you’ll work on
Full postingDefine the target data architecture for multi-cloud cost, usage, and financial datasets
Design conceptual, logical, and physical data models optimized for BigQuery
Design safe, read-only BigQuery datasets and MCP tools for agent access
Create source-to-target mappings, data contracts, and lineage documentation
Review SQL, pipelines, and schemas for performance and cost efficiency
From the employer’s posting
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
design safe, discoverable, read-only BigQuery datasets and MCP tools for agent access
Create source-to-target mappings, transformation rules, data contracts, lineage documentation, and business definitions
Review SQL queries, data pipelines, schemas, and integration designs for quality, scalability, maintainability, performance, and cost efficiency
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Oct 14, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- BigQuery
- GCP
Where the posting names them
We are looking for a visionary and hands-on Senior Data Architect to lead our data modeling, integration patterns, and semantic dataset initiatives. In this role, you will be the bridge between traditional data engineering and cutting-edge AI systems. You will work side-by-side with our AI Engineers to design agent-ready datasets and Model Context Protocol (MCP) data-access patterns, while partnering with Data Engineers to establish robust, scalable ingestion, transformation, quality, and monitoring standards. 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. Req.#1110315973 Responsibilities 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 Requirements 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
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
Already applied? Track this application
Has EPAM Systems posted this job before?
Yes. We’ve followed one other Senior Data Architect job there since November 2026. It is still up.
About the role
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
US government figures for computer and information systems managers
- Median yearly pay, May 2025
- $175,140
- Expected change, 2025 to 2035
- 15.8% more jobsagainst 3.5% for all jobs
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
Job description
We are looking for a visionary and hands-on Senior Data Architect to lead our data modeling, integration patterns, and semantic dataset initiatives. In this role, you will be the bridge between traditional data engineering and cutting-edge AI systems. You will work side-by-side with our AI Engineers to design agent-ready datasets and Model Context Protocol (MCP) data-access patterns, while partnering with Data Engineers to establish robust, scalable ingestion, transformation, quality, and monitoring standards. 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. Req.#1110315973 Responsibilities 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 Requirements 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
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