Data Engineer

BLN24•Mclean, VA

5d•Hybrid

About The Position

BLN24 is seeking a Data Engineer to support the design and development of a data integrity and quality monitoring application. This role focuses on supporting data pipelines, validation frameworks, and user-facing tools that enable stakeholders to assess, validate, and act on data quality issues. The Data Engineer will contribute to both backend data processing and frontend application logic, ensuring that data is accurate, traceable, and usable within a web-based decision-support environment. The ideal candidate combines strong engineering skills with an understanding of data quality, metrics, and user workflows.

Requirements

2–4+ years of experience in data engineering, analytics engineering, or similar roles.
Strong proficiency in Python and SQL for data transformation and analysis.
Experience building data-driven applications (e.g., Dash or similar frameworks).
Familiarity with data quality concepts such as validation, completeness, and anomaly detection.
Understanding of data modeling and structuring datasets for analytics use cases.
Ability to translate analytical needs into technical implementations.
Experience working in Agile environments and collaborating with cross-functional teams.

Nice To Haves

Experience with Dash Mantine Components or similar UI libraries.
Familiarity with cloud platforms (AWS or similar).
Experience with data lineage, traceability, or audit frameworks.
Experience supporting decision-support tools or operational analytics applications.

Responsibilities

Design and implement data pipelines to ingest, transform, and prepare data for quality validation and analysis.
Develop logic to assess data integrity, completeness, and correctness across datasets.
Build and support a web-based application using Dash and Mantine components to enable users to explore and validate data.
Implement data validation rules, anomaly detection checks, and quality metrics.
Translate business and analytical requirements into structured data models, fields, and transformations.
Develop features that allow users to filter, review, and take action on flagged data issues.
Ensure traceability of data through transformation steps, supporting auditability and reproducibility.
Collaborate with stakeholders to understand workflows and design intuitive data review experiences.
Optimize performance of queries and backend processes supporting the application.
Support deployment and integration of application components within cloud environments.
Contribute to documentation of data logic, validation rules, and system behavior.