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Data Architect (AWS Databricks)

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Onsite

Data Architect (AWS Databricks)

Tredence Inc.

Data Architect (AWS Databricks)

Skills

Data EngineeringDatabricksAIAWS

5 days ago

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Important information


Contract type:

Permanent

Location:

United States

Work mode:

Onsite

Published on:

17 October 2026

What they need


Data Architect — AWS & Databricks

AWS + Databricks Ecosystem | Data Engineering Leadership | Full-Time

ROLE OVERVIEW

We are looking for a Data Architect to own the design and delivery of modern data platforms built on AWS and Databricks. This role combines deep hands-on engineering with team leadership, covering data ingestion, transformation, and warehousing across structured and unstructured sources.

KEY RESPONSIBILITIES

  • Architect and deliver end-to-end data engineering solutions on AWS and Databricks, spanning ingestion, transformation, and warehousing layers.
  • Design scalable pipelines for structured and unstructured data, balancing reliability, performance, and cost efficiency.
  • Lead and mentor a team of data engineers, overseeing sprint planning, code quality, and technical delivery.
  • Partner with architects and business stakeholders to translate requirements into robust, production-ready designs.
  • Establish and maintain standards for data quality, governance, and platform scalability.

WHAT WE'RE LOOKING FOR

MUST-HAVE SKILLS

AWS Services — Hands-on expertise across core AWS services spanning compute, storage, orchestration, and security in production environments.

Databricks Platform — Deep proficiency in Databricks, including Delta Lake, Unity Catalog, cluster management, and job orchestration at scale.

Data Engineering Lifecycle — Proven ability to design and operate ingestion, transformation, and warehousing pipelines across structured and unstructured data.

Team Leadership — Experience leading and mentoring data engineering teams, managing delivery timelines, and driving technical quality.

GOOD-TO-HAVE SKILLS

Agentic AI Exposure — Working understanding of agentic AI concepts and frameworks, and how they apply within modern data platforms.

Manufacturing Industry Knowledge — Familiarity with manufacturing industry data landscapes, processes, and common use cases.

Data Modelling — Understanding of data modelling principles, including dimensional and canonical modelling approaches.

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