Data Architect / Principal Data Engineer III

Title: Data Architect / Principal Data Engineer III

Location: Farmington Hills Michigan 48331

Must Have

• Amazon RDS
• DynamoDB
• ETL/ELT pipelines and data-processing workflows
• Experience with data modeling, APIs, JSON, Parquet, and pgvector
• PostgreSQL
• Python
• S3
• SQL

Senior Data Engineer

Wealth & Asset Management

Position Summary

The Senior Data Engineer is responsible for designing, building, and supporting scalable and 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.

Key Responsibilities

• Design and develop scalable data ingestion and ETL/ELT pipelines.
• Build solutions for structured, semi-structured, and unstructured data.
• Develop reusable data-processing and transformation 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, where applicable.
• Prepare and transform data for application, search, analytics, and AI-enabled use cases.
• Support vector-based retrieval using PostgreSQL/pgvector, where required.
• Implement data quality, validation, reconciliation, metadata, lineage, retention, and governance controls.
• Implement secure data access and encryption for sensitive information.
• Optimize data pipelines and databases for performance, reliability, scalability, and cost.
• Implement monitoring and troubleshoot data-processing and integration issues.
• Collaborate with application, AI, DevOps, architecture, cybersecurity, and business teams.

Required Qualifications

• 8+ years of experience in data engineering, data integration, or related roles.
• Strong hands-on experience with Python, SQL, PostgreSQL, Amazon RDS, DynamoDB, and S3.
• Experience developing and supporting ETL/ELT pipelines and data-processing workflows.
• Experience with data modeling, APIs, JSON, Parquet, and pgvector.
• Strong understanding of data quality, metadata, governance, security, and operational monitoring.
• Experience working within large enterprise or regulated environments.
• Experience supporting document-processing and AI-enabled data solutions.