Data Engineer

Priwils•Hyattsville, MD

12h•Remote

About The Position

The Software Developer II provides advanced data development and engineering support for District Health Information System (DHIS) survey systems. This role focuses on data ingestion, ETL development, automation, performance optimization, and cloud-based data processing to support data production, analysis, and reporting activities. Key Responsibilities This is a Remote (work from home) position.

Requirements

Minimum four (4) years of college-level education and 3–5 years of relevant experience.
Proficiency in Python for application development, automation, and data processing.
Experience with Microsoft SQL Server, including writing complex SQL queries, optimizing performance, and ensuring data integrity.
Hands-on experience with cloud application development, deployment, management, and security.
Strong understanding of ETL pipelines, data integration, and workflow automation.
Ability to process complex datasets and generate actionable insights efficiently.
Knowledge of data governance, compliance standards, and security best practices.
Experience collaborating with IT support staff, analysts, and business stakeholders.
Familiarity with version control (Git), CI/CD pipelines, and DevOps best practices.

Nice To Haves

Some familiarity with R programming (preferred but not required).

Responsibilities

Develop and maintain data ingestion and integration pipelines using Python and other ETL tools.
Provide database support by studying, loading, modifying, and enhancing the MID database schema using SQL DDL statements.
Design, implement, and optimize ETL workflows to transform raw data into structured formats suitable for editing, analysis, and reporting.
Develop Python scripts and SQL stored procedures to perform data editing, transformation, updates, and corrections based on business rules.
Perform data analysis and reporting using Python, SQL, and visualization tools (e.g., Power BI) to support decision-making.
Automate repetitive processes such as data processing, file transfers, and reporting using Python scripts, SQL procedures, and other tools.
Monitor and optimize database queries, ETL jobs, and cloud-based processing for performance tuning and efficiency.
Implement robust error handling, logging, and alerting mechanisms to ensure data pipeline reliability and timely issue resolution.
Maintain clear documentation of data workflows, processes, and scripts.
Collaborate with cross-functional teams to support data production activities and data innovation initiatives.