Tredence Inc.
Data Architect (AWS Databricks)
Skills
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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