Data Engineer – Classical Statistics & Machine Learning

Key Responsibilities

• 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

Required Qualifications

• 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