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AWS Data & AI Tech Lead

BuautUnited States🇺🇸United StatesPosted 2 Sept 2026

Why This Role Stands Out

Lead innovative data and AI initiatives leveraging your deep AWS expertise and Python skills in a hybrid environment that offers flexibility and significant growth potential. You'll thrive here if you're a seasoned technical leader with a passion for building robust, scalable data platforms and a knack for mentoring distributed teams. This is an exciting opportunity to shape cutting-edge solutions and advance your career.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
20 hours ago
FastAPIAPI GatewayAWSETLAirflowApacheCDKData PrivacyGitPower BIPythonReact

Job Description

Job Title: AWS Data & AI Technical Lead
Location: Remote
Required skills and experience
  • 10+ years in technology delivery, including 4+ years as a technical lead, architect, or senior engineer on data platform or data engineering programs.
  • Deep, hands-on AWS experience: S3, S3 Tables, Glue, Athena, Lambda, Step Functions, DMS, Lake Formation, IAM, API Gateway, CloudFront, Secrets Manager, CloudWatch, and ECR. You should be able to read CloudTrail and resolve an access-denied path unaided.
  • Apache Iceberg: practical working knowledge of table formats, catalogs, and engine compatibility.
  • AWS CDK (Python): you build and maintain infrastructure as code as a matter of course.
  • Python at production standard: Lambda handlers, PySpark ETL, CDK stacks, and test suites that you write and review, not merely read.
  • Orchestration with Airflow or MWAA.
  • Enterprise access control at scale: RBAC and ABAC models, fine-grained filtering, identity federation, and the operational reality of managing grants across many principals and resources.
  • Experience leading distributed engineering teams across time zones, with Git and disciplined commit and review practice.
  • This role produces a significant volume of design documentation and client-facing material, and the quality of that writing matters.
Preferred
  • GenAI delivery experience: Amazon Bedrock, agent runtimes, embedding models, vector search, and RAG system design.
  • Document intelligence: OCR, layout-aware extraction, chunking strategy, and ontology design over unstructured content.
  • Identity federation with Microsoft Entra ID and AWS IAM Identity Center, including trusted identity propagation.
  • SageMaker Unified Studio, Amazon DataZone, or comparable data catalog and subscription platforms.
  • Semantic layer and BI experience with Power BI, Cognos, or equivalent, including correct treatment of ratio measures across aggregation levels.
  • React and FastAPI, sufficient to build and maintain internal tooling.
  • Data cataloguing and data privacy platforms such as Atlan or BigID.
  • Consulting or client-services delivery within a large enterprise account.
How we work
  • Establish ground truth before building. Audit the live environment first and separate what was measured from what was assumed.
  • Verify against real state, not a green pipeline. A successful deployment is not evidence that the thing works.
  • Raise uncertainty early. Stopping to ask is always preferred over guessing and continuing.
  • Infrastructure as code, without exception. Manual changes are permitted only as approved, temporary steps to validate a fix before it is codified.
  • Leave no trace in shared environments, and be able to prove it.

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