Pay
In line with similar jobs
$150K–$200K against $154K–$225K, the middle half of what similar US postings state.
From 2,958 postings in the last year. Stated ranges, not salaries paid.
AI Data Architect
Reports into Rochester, NY
Open 5 months, first seen by us 155 days ago- Pay
USD 150,000–200,000/year — pay source
Salary 150,000 – 200,000 USD per year Employment type
Read the full posting- Work setting
Remote — work setting source
Work setup in the employer’s posting: remote
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-Time
Employment type in the employer’s posting: Full-Time
From the employer’s posting
What you’ll work on
Full postingDesign S3 data lakes with partitioning, lifecycle policies, and encryption
Implement bronze/silver/gold medallion architecture on Redshift, Snowflake, or Databricks
Build AWS Glue ETL pipelines in Python Shell and Spark
Design RAG systems using Bedrock Knowledge Base and OpenSearch vector search
Lead data discovery sessions and present architecture recommendations to clients
From the employer’s posting
Design and build S3 data lakes with multi-zone organization, partitioning strategies, lifecycle policies, and encryption
Implement medallion architecture (bronze/silver/gold) for data warehouses on Redshift, Snowflake, or Databricks
Build AWS Glue ETL pipelines (Python Shell and Spark) with incremental extraction, Data Catalog management, and optimized Parquet output
Design RAG systems using Bedrock Knowledge Base with OpenSearch Serverless vector search and Titan Embeddings
Lead data discovery sessions with client stakeholders and present architecture recommendations to technical and business audiences
What you’ll bring
All qualificationsCore experience
- 5+ years professional IT experience, 2+ years professional AWS experience
- Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs
- SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization
- Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries
Qualification wording
5+ years professional IT experience, 2+ years professional AWS experience
Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs
SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization
Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries
Posting record
Report an errorOpen 5 monthsWe first saw this posting 155 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026155 days ago
- Recorded sightings
- 41
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Jul 2, 2025
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 155
Up longer than most. Similar jobs usually come down between day 7 and day 53.
From 7,804 similar postings that came down. Coming down is not always a hire.
Innovative Solutions is hiring
2 open jobs
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
- AWS
- Databricks
- Delta Lake
- DynamoDB
- NoSQL
- PostgreSQL
- Power BI
- Redshift
- S3
- SageMaker
- Snowflake
- Spark
- Terraform
- PySpark
Where the posting names them
- Implement medallion architecture (bronze/silver/gold) for data warehouses on Redshift, Snowflake, or Databricks - Build AWS Glue ETL pipelines (Python Shell and Spark) with incremental extraction, Data Catalog management, and optimized Parquet output - Design star/snowflake schemas, materialized views, and gold-layer models optimized for BI consumption (QuickSight, PowerBI)
- At least one AWS Professional-level certification (Solutions Architect Professional or Data Engineer Specialty preferred) - Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs - SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization
- Dimensional modeling and medallion architecture (bronze/silver/gold) on Redshift, Snowflake, or Databricks, including materialized views and incremental refresh patterns - AWS Glue ETL (Python Shell and Spark), Glue Data Catalog, and crawlers - S3 data lake architecture with partitioning, lifecycle policies, and encryptions
- Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs - SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization - Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries
As a Data/AI Architect, you'll design and build data-driven cloud architectures on AWS — from S3 data lakes and Glue ETL pipelines to data warehouses and RAG-powered AI systems. You'll own the full data stack across a variety of industries and projects: one engagement you're designing a Redshift data warehouse with medallion architecture processing 31M transactions/month, the next you're building a Bedrock Knowledge Base with OpenSearch vector search. Real ownership, real variety. What You'll Do:
- Lead data discovery sessions with client stakeholders and present architecture recommendations to technical and business audiences - Mentor delivery team members on data architecture patterns and AWS data services - Contribute to R&D projects evaluating emerging AWS data and AI capabilities
- Mentor delivery team members on data architecture patterns and AWS data services - Contribute to R&D projects evaluating emerging AWS data and AI capabilities Required Skills:
Required Skills: - 5+ years professional IT experience, 2+ years professional AWS experience - At least one AWS Professional-level certification (Solutions Architect Professional or Data Engineer Specialty preferred)
- 5+ years professional IT experience, 2+ years professional AWS experience - At least one AWS Professional-level certification (Solutions Architect Professional or Data Engineer Specialty preferred) - Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs
- SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization - Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries - Dimensional modeling and medallion architecture (bronze/silver/gold) on Redshift, Snowflake, or Databricks, including materialized views and incremental refresh patterns
- Amazon SageMaker for ML training, Model Registry, and inference - AWS HealthLake, FHIR R4 transformation, and HIPAA-compliant data pipelines - Document AI with Amazon Textract and Comprehend
- Design and build S3 data lakes with multi-zone organization, partitioning strategies, lifecycle policies, and encryption - Implement medallion architecture (bronze/silver/gold) for data warehouses on Redshift, Snowflake, or Databricks - Build AWS Glue ETL pipelines (Python Shell and Spark) with incremental extraction, Data Catalog management, and optimized Parquet output
- Design star/snowflake schemas, materialized views, and gold-layer models optimized for BI consumption (QuickSight, PowerBI) - Configure data warehouse platforms — Redshift with Zero-ETL from Aurora, Snowflake with Snowpipe, Databricks with Delta Lake and Auto Loader - Design RAG systems using Bedrock Knowledge Base with OpenSearch Serverless vector search and Titan Embeddings
- Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries - Dimensional modeling and medallion architecture (bronze/silver/gold) on Redshift, Snowflake, or Databricks, including materialized views and incremental refresh patterns - AWS Glue ETL (Python Shell and Spark), Glue Data Catalog, and crawlers
- Build AWS Glue ETL pipelines (Python Shell and Spark) with incremental extraction, Data Catalog management, and optimized Parquet output - Design star/snowflake schemas, materialized views, and gold-layer models optimized for BI consumption (QuickSight, PowerBI) - Configure data warehouse platforms — Redshift with Zero-ETL from Aurora, Snowflake with Snowpipe, Databricks with Delta Lake and Auto Loader
- Document AI with Amazon Textract and Comprehend - Amazon Athena, QuickSight, or PowerBI integration - Terraform or CloudFormation for data infrastructure as code
What You'll Do: - Design and build S3 data lakes with multi-zone organization, partitioning strategies, lifecycle policies, and encryption - Implement medallion architecture (bronze/silver/gold) for data warehouses on Redshift, Snowflake, or Databricks
- AWS Glue ETL (Python Shell and Spark), Glue Data Catalog, and crawlers - S3 data lake architecture with partitioning, lifecycle policies, and encryptions Preferred:
- Architect document AI pipelines using Textract, Comprehend, and Bedrock for entity extraction - Design SageMaker ML pipelines for training, Model Registry, and inference - Lead data discovery sessions with client stakeholders and present architecture recommendations to technical and business audiences
- RAG systems with Bedrock Knowledge Base and OpenSearch Serverless vector search - Amazon SageMaker for ML training, Model Registry, and inference - AWS HealthLake, FHIR R4 transformation, and HIPAA-compliant data pipelines
- Amazon Athena, QuickSight, or PowerBI integration - Terraform or CloudFormation for data infrastructure as code - Step Functions, EventBridge, and Lambda for event-driven pipeline orchestration
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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
As a Data/AI Architect, you'll design and build data-driven cloud architectures on AWS — from S3 data lakes and Glue ETL pipelines to data warehouses and RAG-powered AI systems. You'll own the full data stack across a variety of industries and projects: one engagement you're designing a Redshift data warehouse with medallion architecture processing 31M transactions/month, the next you're building a Bedrock Knowledge Base with OpenSearch vector search. Real ownership, real variety.
What You'll Do:
- Design and build S3 data lakes with multi-zone organization, partitioning strategies, lifecycle policies, and encryption
- Implement medallion architecture (bronze/silver/gold) for data warehouses on Redshift, Snowflake, or Databricks
- Build AWS Glue ETL pipelines (Python Shell and Spark) with incremental extraction, Data Catalog management, and optimized Parquet output
- Design star/snowflake schemas, materialized views, and gold-layer models optimized for BI consumption (QuickSight, PowerBI)
- Configure data warehouse platforms — Redshift with Zero-ETL from Aurora, Snowflake with Snowpipe, Databricks with Delta Lake and Auto Loader
- Design RAG systems using Bedrock Knowledge Base with OpenSearch Serverless vector search and Titan Embeddings
- Architect document AI pipelines using Textract, Comprehend, and Bedrock for entity extraction
- Design SageMaker ML pipelines for training, Model Registry, and inference
- Lead data discovery sessions with client stakeholders and present architecture recommendations to technical and business audiences
- Mentor delivery team members on data architecture patterns and AWS data services
- Contribute to R&D projects evaluating emerging AWS data and AI capabilities
Required Skills:
- 5+ years professional IT experience, 2+ years professional AWS experience
- At least one AWS Professional-level certification (Solutions Architect Professional or Data Engineer Specialty preferred)
- Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs
- SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization
- Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries
- Dimensional modeling and medallion architecture (bronze/silver/gold) on Redshift, Snowflake, or Databricks, including materialized views and incremental refresh patterns
- AWS Glue ETL (Python Shell and Spark), Glue Data Catalog, and crawlers
- S3 data lake architecture with partitioning, lifecycle policies, and encryptions
Preferred:
- RAG systems with Bedrock Knowledge Base and OpenSearch Serverless vector search
- Amazon SageMaker for ML training, Model Registry, and inference
- AWS HealthLake, FHIR R4 transformation, and HIPAA-compliant data pipelines
- Document AI with Amazon Textract and Comprehend
- Amazon Athena, QuickSight, or PowerBI integration
- Terraform or CloudFormation for data infrastructure as code
- Step Functions, EventBridge, and Lambda for event-driven pipeline orchestration
The salary range provided is a general guideline. When extending an offer, Innovative considers factors including, but not limited to, the responsibilities of the specific role, market conditions, geographic location, as well as the candidate’s professional experience, key skills, and education/training.
Salary
150,000 – 200,000 USD per year
Employment type
Full-Time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Salary 150,000 – 200,000 USD per year Employment type
- Location & working pattern
Reports into Rochester, NY
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.