Data Engineer – Classical Statistics & Machine Learning

BLN24; McLean (VA); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

Remote working opportunities

Collaborative environment

Job summary

BLN24 is looking for a mid-level Data Engineer to support a data modernization effort for a federal agency. This hybrid role encompasses data engineering and applied data science, requiring strong Python skills and knowledge of ML techniques.

The ideal candidate will design ETL pipelines, apply statistical methods, and collaborate with SMEs to deliver operational insights. U.S. citizenship is required, and the role offers flexibility in work arrangements.

Qualifications

• 3-5 years of experience in data engineering and data science/statistical analysis.
• Strong proficiency in Python, including data engineering and statistical libraries.
• Experience with ETL/ELT pipelines and data validation.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines.
• Apply classical statistical methods to analyze operational data.
• Develop machine learning models balancing performance and interpretability.

Skills

Data engineering libraries (pandas, PySpark)

Statistical/ML libraries (scikit-learn, statsmodels)

SQL and relational/distributed data systems

Statistical methods (hypothesis testing, regression)

Communication skills

Education

Bachelor's degree in Data Science, Statistics, Computer Science, Engineering

Tools

Databricks

Airflow

Job description

Job Title: Data Engineer

Company: BLN24

About Us: We find strength in teamwork-a better you is a better us. BLN24 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

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.

Key Responsibilities

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

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

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

What BLN24 brings to the Game

• 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.

BLN24 is an Equal Opportunity Employer. We believe people are our strength and understand diverse talents are key to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.