Data Engineer – Remote

About The Role

We are seeking a mid-level Data Engineer to join our team supporting a large-scale data and analytics platform modernization project for a federal statistical agency client. This hybrid role involves building and maintaining robust data pipelines that facilitate the ingestion and transformation of data from multiple sources into a centralized data platform. The position also encompasses applied data science responsibilities, including statistical analysis and machine learning model development, to derive actionable insights from operational data.

The ideal candidate will be comfortable working in a collaborative environment, engaging with subject matter experts across different program areas to understand source data, ensure data quality, and support operational decision-making through advanced analytical techniques. This role offers the opportunity to work within modern cloud-based architectures, contributing to high-impact government initiatives that improve data-driven policymaking and operational efficiency.

Qualifications

• Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, or related field (or equivalent experience)
• 3–5 years of experience in data engineering and data science/statistical analysis
• Proficiency in Python, including libraries such as pandas, PySpark, scikit-learn, and statsmodels
• Hands-on experience designing, building, and maintaining ETL/ELT pipelines
• Strong understanding of classical statistical methods and machine learning techniques
• Experience working with SQL and relational/distributed data systems
• Knowledge of federal data environments, including data sensitivity and access controls
• Excellent communication skills, capable of translating technical concepts for non-technical stakeholders

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines to ingest data from diverse source systems into the central data platform
• Create and optimize data ingestion workflows for batch and near-real-time data sources
• Implement data validation, cleaning, and transformation processes to ensure high data quality and consistency
• Operate within modern lakehouse/cloud architectures, ensuring pipeline performance and reliability
• Build and maintain data models and schemas to support downstream analytics and reporting needs
• Monitor data pipelines, troubleshoot failures, and establish logging and alerting mechanisms for data quality issues
• Document data lineage, transformation logic, and pipeline architecture for governance and reproducibility
• Apply classical statistical methods to identify trends, anomalies, and outliers within operational data
• Design benchmarking approaches comparing production data against historical or external references
• Develop and evaluate machine learning models, ensuring interpretability and stakeholder understanding
• Investigate flagged anomalies to identify root causes and contributing factors
• Collaborate with SMEs to translate operational questions into analytical approaches and communicate findings effectively
• Ensure compliance with data sensitivity classifications and governance requirements during analysis and modeling
• Partner with visualization teams to present statistical and ML outputs clearly to stakeholders

Benefits

• Competitive salary and comprehensive benefits package
• Flexible work arrangements, including remote work opportunities
• Professional development and continuous learning environment
• Opportunity to work on impactful federal projects supporting national data modernization initiatives
• Collaborative and inclusive work culture that values diversity and innovation