Data Engineer - Classical Statistics & Machine Learning

• 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, statsmodels)
• 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 programs
• Familiarity with Databricks or modern lakehouse architectures (Spark, Delta Lake, etc.)
• Experience with workflow orchestration tools (e.g., Airflow, Databricks Workflows)
• Experience designing anomaly-detection or outlier-detection approaches beyond standard threshold-based methods
• Exposure to disclosure avoidance concepts or working with regulated/protected government data
• Experience working across multiple coding environments (Python, R, SAS) within the same analytics platform
• Background in requirements gathering or systems design for enterprise data platforms

Work Environment:

• Contract position supporting a federal agency data modernization engagement
• Collaborative, cross-functional environment working alongside data engineers, data scientists, architects, and program SMEs
• Requires U.S. citizenship and ability to obtain a public trust or other clearance/suitability determination typical of federal contractor engagements

• You can join one of the fastest growing companies headquartered in the Washington DC Metro Area. We give you the opportunity to work in different sectors, so you have the chance at variety while maintaining stability.
• Flexibility at BLN24 allows each individual the opportunity to balance quality work and their personal lives. Depending on projects, we allow remote working opportunities so you can always be in the game no matter where you call home.