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# Sr. IT Data Engineer

- **Company:** Horizontal Talent
- **Location:** Washington, DC, United States
- **Experience:** Expert
- **Salary:** $85,280.0 - $162,240.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Data Architecture, Information Engineering, Data Systems, Relational Databases, Database Design, Distributed Systems, Graph Database, Python (Programming Language), PostgreSQL, Machine Learning, MySQL, NoSQL, SQL Databases, Data Streaming, Scripting, Snowflake, Data Delivery, Data Pipelines
- **Published:** August 28, 2026
- **Apply:** https://www.jofdav.com/jobs/59423137-sr-it-data-engineer

## About the Role

* Proven experience in data modeling, database design, and administration.
 * Strong proficiency in SQL and relational database platforms like PostgreSQL and MySQL.
 * Advanced knowledge of Python, R, and other scripting languages for data engineering.
 * Experience with large-scale data systems and distributed computing.
 * Ability to design and communicate enterprise information architecture effectively.

Preferred Skills

 * Experience with cloud technologies such as AWS, Microsoft Azure, and Snowflake.
 * Familiarity with NoSQL and graph database technologies.
 * Knowledge of machine learning model development and deployment.
 * Experience in a research environment or with economic/financial data.
 * Understanding of time series data and related analytical techniques.

## Description

Join our dynamic team as a Senior IT Data Engineer and play a crucial role in transforming data architecture and optimizing data pipelines for economic policy and research. This position offers the chance to work with cutting-edge technologies and contribute to impactful data initiatives.
Responsibilities

 * Design and enhance data architecture to support economic policy and research teams.
 * Develop and optimize data pipelines for efficient data flow and collection.
 * Collaborate with economists and technical experts to ensure optimal data delivery architecture.
 * Improve existing data systems and build new ones from the ground up.
 * Conduct root cause analysis to identify opportunities for data system improvements.


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