What are the responsibilities and job description for the Data Engineer position at Lucid Technologies?
Job Title: Data Engineer Location: Woodlawn, MD - Onsite
Description of Work:
Data Engineer to support a federal agency in modernizing its fraud control strategy. This senior technical role is part of
a high impact team responsible for redesigning and optimizing fraud detection pipelines and workflows that safeguard
federal benefits and adapt to evolving data sources. You will lead the design and implementation of scalable, secure, and flexible data engineering solutions while collaborating closely with data scientists, analysts, and federal stakeholders to ensure the iClaim model infrastructure is robust, adaptable, and future ready.
Expertise in SQL and ETL workflows with experience in distributed database technologies (Redshift, Snowflake, BigQuery).
Experience with Apache Airflow or similar orchestration frameworks.
Strong Python development skills, especially with pandas, NumPy, and automation libraries.
Hands-on experience building data quality checks and validation frameworks.
Familiarity with GitHub, Bitbucket, or similar version control tools.
Knowledge of logging, monitoring, and error handling approaches in production workflows.
Understanding of modular design, code optimization, and Agile methodologies.
Ability to obtain and maintain US Public Trust Clearance
Basic Qualifications: Minimum knowledge, skills, abilities needed.
Bachelor s degree and 7 years of relevant experience.
Engineer, transform, and manage large datasets from diverse sources while ensuring data integrity, reliability, and consistency.
Design, optimize, and maintain complex SQL queries for ETL operations across distributed databases.
Develop and automate ETL pipelines using platforms such as Apache Airflow, ensuring high availability and resilience.
Write modular, well documented Python code for data processing, feature engineering, and workflow orchestration.
Implement comprehensive data validation, cleansing, and quality checks throughout all pipeline stages.
Apply software engineering best practices including version control, code reviews, logging, monitoring, and error handling.
Collaborate with data scientists to optimize model pipelines for performance, maintainability, and scalability.
Prepare and maintain technical documentation such as data dictionaries, model specs, workflow guides, and architectural artifacts.
Support Agile development cycles and contribute to the documentation and refinement of model workflows and data processes.
Participate in deployment activities and conduct rigorous testing, validation, and post implementation reviews Maintain clear
Preferred Qualifications: Candidates with these skills will be given preferential consideration.
Experience supporting federal agencies, particularly in fraud or anomaly detection.
Familiarity with cloud platforms (AWS, Azure, Google Cloud Platform) and containerization tools (Docker, Kubernetes).
Exposure to CI/CD, DevOps practices, and infrastructure as code.
Knowledge of data governance, PII handling, and federal security standards.
Experience with model documentation, feature tracking, and model lifecycle management.
Salary : $60 - $65