Senior Data Engineer: AWS ETL & Data Modeling

Quantum Technologies. LLC; Sunnyvale (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Quantum Technologies. LLC is seeking a Senior Data Engineer to design, build, and support scalable, secure data solutions for enterprise Wealth & Asset Management applications.

The role focuses on data ingestion, transformation, storage, integration, quality, governance, and processing across structured and unstructured data sources. The ideal candidate will develop reusable data pipelines using Python and SQL, design data models with PostgreSQL, Amazon RDS, DynamoDB, and Amazon S3, and support

Qualifications

• 8+ years of experience in data engineering or related roles.
• Hands-on experience with Python, SQL, PostgreSQL, AWS services.
• Experience developing and supporting ETL/ELT pipelines and data-processing workflows.
• Experience with data modeling, APIs, JSON, Parquet, and pgvector.
• Understanding of data quality, metadata, governance, security, and operational monitoring.

Responsibilities

• Design and develop scalable data ingestion and ETL/ELT pipelines.
• Build solutions for structured, semi-structured, and unstructured data.
• Develop reusable data-processing components using Python and SQL.
• Design and support data models using PostgreSQL, Amazon RDS, DynamoDB, and Amazon S3.
• Support document ingestion and extraction using Amazon Textract.
• Prepare and transform data for analytics, search, and AI use cases.
• Implement data quality, lineage, and governance controls.
• Collaborate with cross-functional teams including AI, DevOps, and cybersecurity.

Skills

AWS

SQL

Python

Distributed Computing

Tools

PostgreSQL

Amazon RDS

DynamoDB

Amazon S3

Amazon Textract

Job description

Quantum Technologies. LLC is seeking a Senior Data Engineer to design, build, and support scalable, secure data solutions for enterprise Wealth & Asset Management applications.

The role focuses on data ingestion, transformation, storage, integration, quality, governance, and processing across structured and unstructured data sources. The ideal candidate will develop reusable data pipelines using Python and SQL, design data models with PostgreSQL, Amazon RDS, DynamoDB, and Amazon S3, and support