Data Engineer I

eSimplicity•Columbia, MD

3d•Hybrid

About The Position

eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow. eSimplicity is seeking a skilled and motivated Data Engineer to join our data analytics team. In this role, you will support the design, build, and optimize scalable data pipelines and services that power advanced analytics and machine learning solutions. This role emphasizes data quality, performance, and interoperability in modern cloud environments. The engineer will ensure secure, efficient, and reproducible data workflows that support predictive modeling, real-time monitoring, and actionable insights. Successful candidates will be eligible to hold a U.S. Federal Public Trust security clearance. This position is contingent upon contract award.

Requirements

Bachelor’s degree or equivalent professional experience in Data Science, Computer Science, Engineering, or related field.
All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation.
3+ years developing and deploying advanced statistical and machine learning models or supporting data pipelines for such models.
Proficiency in Python (Pandas required; scikit-learn, NumPy, and related libraries preferred).
Strong SQL skills and experience integrating data from relational databases.
Familiarity with open-source data processing libraries (Pandas, PyTorch, Tensorflow etc.).
Open-source frameworks for production-grade data pipelines.
ETL development using Python and Microsoft technologies.
Data validation, schema enforcement, and quality assurance.
API development within Microsoft ecosystem.
Performance optimization, logging, and monitoring for large-scale systems.
Azure Data Lake Storage integration and Azure SQL connectivity.
Workflow orchestration with Azure Data Factory.
Deployment and operation of Python-based data services in Azure.
Strong attention to detail with a commitment to delivering high-quality and accurate work.
Excellent communication skills, both written and verbal, with the ability to collaborate effectively across teams.
Proven ability to manage time and prioritize tasks in a fast-paced environment.
Demonstrated problem-solving skills with a proactive and solution-oriented mindset.

Nice To Haves

Knowledge of data engineering patterns for scalable, secure systems in regulated environments.
Hands-on experience in cloud environments (Azure); Microsoft Data Engineer certification is advantageous.

Responsibilities

Develop production-grade ETL workflows using Python and Microsoft-based frameworks to ingest, transform, and validate large-scale structured and unstructured data.
Implement schema enforcement, data validation, and quality checks to maintain integrity across diverse sources.
Optimize pipelines for performance, scalability, and fault tolerance using open-source and cloud-native patterns.
Manage Azure-based data solutions, including Data Lake Storage, Azure SQL, and cloud storage access from Python services.
Deploy workflow orchestration using Azure Data Factory or Foundry for scheduling, monitoring, and automation.
Ensure secure integration of APIs and services within the Microsoft ecosystem for seamless data exchange.
Build Python-based data services leveraging libraries such as Pandas, Pytorch, and other open-source frameworks for high-performance processing.
Implement logging, monitoring, and performance tuning for robust operational reliability.
Develop API endpoints and microservices to enable interoperability with analytics and ML platforms.
Work closely with data scientists, analysts, and cloud architects to deliver clean, reliable data for predictive modeling and real-time dashboards.
Apply data governance best practices, ensuring compliance, reproducibility, and auditability across workflows.
Contribute to Agile team processes, driving iterative improvements and shared problem-solving.
Work in Agile teams; drive iterative delivery, joint problem-solving, and continuous improvement.
Engage closely with project managers, technical leads, client representatives, and cross-functional teams to provide timely updates, resolve issues, and ensure alignment with business goals.
Translate technical specifications into code and design documents.