Data Engineer
Role details
Job location
Tech stack
Job description
BLN24 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., Data Engineering
- 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
Data Science / Statistics & ML
- 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
Requirements
- 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
Preferred Qualifications:
- 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
Benefits & conditions
Invitation for Job Applicants to Self-Identify as a U.S. Veteran
- A "disabled veteran" is one of the following:
- a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or
- a person who was discharged or released from active duty because of a service-connected disability.
- A "recently separated veteran" means any veteran during the three-year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service.
- An "active duty wartime or campaign badge veteran" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.
- An "Armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.