Q

Quantiphi

AI-driven digital transformation and automation services

Associate Architect - Multiple Teams

Data EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • At least 5 years of experience designing and implementing data warehouses and data lakes or lakehouses on AWS.
  • Hands-on experience with AtScale or similar semantic-layer tools for governed, business-friendly data access across BI platforms.
  • Experience working with globally distributed teams in collaborative delivery environments.
  • Deep working knowledge of AWS data and analytics services, including Amazon S3, open table formats, AWS Lake Formation, AWS Glue, AWS Lambda, Amazon EMR, AWS Kinesis Data Streams and Firehose, AWS Step Functions or Amazon MWAA, Amazon Redshift, Redshift Spectrum and Serverless, Amazon Athena, Amazon RDS, PostgreSQL, MySQL, Aurora, AWS DMS, Glue Connectors, EventBridge, SNS, and SQS.
  • Strong understanding of semantic modeling, including logical data models, virtual cubes, and centralized metric definitions.
  • Experience optimizing performance using query pushdown, caching, and aggregate awareness on platforms such as Redshift and Athena.
  • Ability to integrate semantic layers with QuickSight, Tableau, or Power BI and enforce row- and column-level security aligned with governance frameworks.
  • Strong programming capability in Python and PySpark for large-scale data processing.
  • Proficiency in writing complex SQL queries and analytical functions, and tuning performance for large datasets.
  • Familiarity with NoSQL databases such as Amazon DynamoDB, MongoDB, or DocumentDB.
  • Strong understanding of partitioning, indexing, scaling approaches, and query optimization techniques.
  • Proven experience architecting and implementing modular, resilient data pipelines using native AWS services.
  • Solid understanding of dimensional, normalized, and lakehouse data-modeling patterns.
  • Solution Architect Associate or Data Engineer Associate certification.

Responsibilities

  • Design and implement data warehouse and data lake or lakehouse architectures on AWS.
  • Build large-scale data lake architectures using Amazon S3 and open table formats.
  • Implement governance and cataloging through AWS Lake Formation.
  • Develop ETL and metadata frameworks using AWS Glue.
  • Use AWS Lambda for serverless data processing and Amazon EMR for distributed data workloads.
  • Enable real-time data pipelines with AWS Kinesis Data Streams and Firehose.
  • Orchestrate pipelines using AWS Step Functions, Amazon MWAA, or similar services.
  • Design and optimize schemas and query performance on Amazon Redshift, including Spectrum and Serverless features.
  • Query large datasets interactively using Amazon Athena.
  • Manage operational databases using Amazon RDS across engines such as PostgreSQL, MySQL, and Aurora.
  • Integrate and migrate data using AWS DMS, Glue Connectors, EventBridge, SNS, and SQS.
  • Integrate semantic layers with BI tools and enforce row- and column-level security in alignment with governance frameworks.
  • Architect and implement modular, resilient data pipelines using native AWS services.

Desired Qualifications

  • Experience working with customers or workloads in the financial services, retail, or consumer packaged goods domains.
  • Exposure to infrastructure-as-code tools such as Terraform and to CI/CD tools.
  • Familiarity with data virtualization tools such as Amazon QuickSight, Power BI, or Tableau, and data governance tools such as Collibra.
  • Experience managing security, monitoring, and compliance with AWS IAM, Secrets Manager, CloudWatch, CloudTrail, and KMS.
  • Experience with AI-assisted development tools such as GitHub Copilot, Amazon Kiro, or similar generative-AI IDEs for developer productivity, code generation, and pipeline acceleration.
  • Exposure to Data Mesh architecture and data governance frameworks.

About the Company

Quantiphi

AI-driven digital transformation and automation services

Quantiphi uses AI and digital engineering to help organizations transform by making products and operations smarter and safer. It partners with clients to understand their challenges and then designs and builds customized AI and automation systems—combining machine learning, data analytics, and software engineering—delivering them under long-term contracts with ongoing support, and collaborating with Google Cloud to integrate technologies. Compared with competitors, it provides end-to-end, tailored solutions across industries rather than one-off projects, backed by long-term relationships and strong technology partnerships to scale. Its goal is to embed AI into products and processes to improve customer experiences and overall efficiency.

Company size
1,001-5,000
Company stage
Series A
Total funding
$23.4M
Headquarters
Marlborough, MA
Founded
2013

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