Data Engineer – Classical Statistics & Machine Learning

Job Title: Data Engineer

Company: BLN
24

About Us: We find strength in teamwork-a better you is a better us
BLN
24 is an award-winning Management Consulting Firm that supports the U.S. Federal Government in successfully achieving their mission and goals. Our service and solutions delivery start with understanding each client’s end-state, and then seamlessly integrating within each Agency’s organization to improve and enhance strategic and technical operations and deployments.

Position Overview:
BLN
24 is seeking a mid-level Data Engineer to support a large-scale data and analytics platform modernization effort for a federal statistical agency client. This is a hybrid role: data engineering(building and maintaining the pipelines that bring data into the platform) and applied data science (using classical statistics and machine learning to analyze that data once it’s available).

The ideal candidate is equally comfortable writing production-grade ingestion and
transformation code as they are designing and validating a statistical or ML model.
This role works closely with SMEs across multiple program areas to understand source data,build reliable ETL/ingestion pipelines, and apply analytical methods — anomaly detection,statistical modeling, and machine learning — to support operational decision-making.

• Design, build, and maintain ETL/ELT pipelines to ingest data from multiple source systems into the platform’s central data store
• Develop and maintain data ingestion workflows for both batch and near-real-time sources
• Implement data validation, cleaning, and transformation logic to ensure data quality and consistency across pipelines
• Work within a modern lakehouse/cloud data architecture, optimizing pipeline performance and reliability
• Build and maintain data models and schemas that support downstream analytics and reporting needs
• Monitor pipeline health, troubleshoot failures, and implement logging/alerting for data quality issues
• Document data lineage, transformation logic, and pipeline architecture for governance and reproducibility

• Apply classical statistical methods (hypothesis testing, regression, time-series analysis, distributional comparisons) to identify trends, anomalies, and outliers in operational data
• Design and implement benchmarking approaches that compare production data against historical, modeled, or external reference values
• Develop and evaluate machine learning models where appropriate, balancing predictive performance with interpretability for non-technical stakeholders
• Investigate flagged anomalies by digging into underlying data to identify root causes and contributing factors
• Work with SMEs to translate operational questions into analytical approaches, and clearly communicate statistical/ML findings and their limitations
• Account for data sensitivity classifications and governance requirements when designing analyses and models
• Collaborate with visualization-focused team members to ensure outputs of statistical/ML work are presented clearly to stakeholders

• Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, or related field (or equivalent experience)
• 3–5 years of experience spanning both data engineering and data science/statistical analysis
• Strong proficiency in Python, including experience with data engineering libraries (e.g., pandas, PySpark) and statistical/ML libraries (e.g., scikit-learn, stats models)
• Hands‑on experience building and maintaining ETL/ELT pipelines, including ingestion, transformation, and validation logic
• Solid grounding in classical statistical methods (hypothesis testing, regression, distributional analysis) and practical machine learning techniques
• Experience working with SQL and relational/distributed data systems
• Ability to work within a federal data environment, including familiarity with data sensitivity tiers and access/disclosure constraints
• Strong communication skills, with the ability to explain technical/statistical concepts to non-technical stakeholders

• Prior experience supporting federal statistical agencies or other federal data…