What are the responsibilities and job description for the Senior Data Engineer (AWS Databricks) position at HMG America?
HMG America LLC is the best Business Solutions focused Information Technology Company with IT consulting and services, software and web development, staff augmentation and other professional services. One of our direct clients is looking for Senior Data Engineer (AWS Databricks) in Seattle, WA / Dallas, TX / Phoenix, AZ / Charleston, SC. Below is the detailed job description.
Title: Senior Data Engineer (AWS Databricks)
Location: Seattle, WA / Dallas, TX / Phoenix, AZ / Charleston, SC (Hybrid . 1-2 days/ week)
Job Description:
ROLE OVERVIEW
We are looking for a Senior Data Engineer to own the design and delivery of modern data platforms built on AWS and Databricks. This role combines deep hands-on engineering, covering data ingestion, transformation, and warehousing across structured and unstructured sources.
KEY RESPONSIBILITIES
- 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.
- 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. | 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. |