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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.
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What you’ll work on

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

From the employer’s posting

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

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

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

Tools in this posting

Python
R
SAS
SQL
Databricks
Delta
Spark
Airflow
pandas
PySpark
scikit-learn

Source

- 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

- 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

- 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

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)

- 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

Job description

View original posting ↗

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:
BLN24 benefits are game changing. We like our team to play hard and that means they need to be taken care of — physically, financially, and emotionally. We make sure to keep them in the game by giving them access to generous medical, dental, and vision plans.

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.

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Pay

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Location & working pattern

McLean, VA

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Work authorization

- 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: BLN24 benefits are game changing. We like our team to play hard and that means they need to be taken care of — physically, financially, and emotionally. We make sure to keep them in the game by giving them access to generous medical, dental, and vision plans.- 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.

Open source at bln24.applytojob.com ↗Read the full description ↓

Status in our records: Active
First seen by us: Jul 2, 2026
Recorded sightings: 44
Last seen by us: Oct 2, 2026

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Data Engineer – Classical Statistics & Machine Learning