SQL Data Engineer for Government Platform

Peregrine Advisors LLC

Washington

Hybrid

USD 120,000 - 140,000

Full time

5 days ago

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Job summary

Peregrine Advisors is seeking a data engineer to design and optimize the data layer for a federal enterprise platform. You will build schemas, ETL/ELT pipelines, and fast queries across Aurora, DynamoDB, and S3, while ensuring data accuracy, security, and auditability.

Hybrid role in the Washington, DC area with the initial engagement requiring US citizenship. Be ready to collaborate with developers, architects, and data engineers, own assigned components from design to maintenance, and leverage

Qualifications

4+ years of data engineering, database development, database engineering, software development, or comparable practical experience.
Bachelor's degree
Working expertise in Amazon Aurora (PostgreSQL-compatible), covering schema design, stored procedures and functions, and indexing
Experience with Amazon DynamoDB, covering data modeling and Global and Local Secondary Indexes (GSIs and LSIs)
SQL skills across complex queries, views, stored procedures, and functions, together with relational design, normalization, and data modeling
Performance and query tuning that addresses slow queries through indexing and execution-plan work
ETL and ELT development and data integration across AWS data services such as Aurora, DynamoDB, and S3
Experience working within data security controls such as encryption, access management, and auditing
Experience loading data from S3 into the platform stores and connecting through ODBC and JDBC
Experience making logical data modeling and database-design decisions for assigned components within an established platform architecture
Proficiency in writing, PowerPoint, and Excel

Responsibilities

Design database schemas, and write the views, stored procedures, functions, and SQL queries.
Build and maintain data pipelines and ETL/ELT processes including loads from S3.
Make queries and databases fast enough for high-volume workloads.
Model data across AWS stores such as Aurora, DynamoDB, and S3.
Keep data accurate and secure, with governed access and auditable changes.
Work out data access patterns and integrations with developers, architects, and data engineers.
Use AI-assisted tools for query, pipeline, and troubleshooting work.