Data Scientist with Security Clearance

BOAB Ventures; McLean (VA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Flexible time off

Full medical coverage

401(k) with company match

Referral bonuses

Performance bonuses

Life insurance and disability coverage

Tuition and training reimbursement

Job summary

BOAB Ventures is seeking a Data Scientist with strong data engineering capabilities to design, build, and maintain scalable data infrastructure and analytics solutions. You will enable data-driven decision-making and support machine learning across structured and unstructured data environments.

The role focuses on developing robust data pipelines, ETL/ELT workflows, and production-ready data modules, collaborating with stakeholders across teams and translating complex data problems into reusable

Qualifications

• Strong data engineering proficiency with ETL/ELT design and implementation.
• Python programming for data processing and automation.
• Strong SQL skills and relational databases.
• Linux environments with advanced Bash scripting.
• Experience building data pipelines with NiFi or similar tools.
• Experience handling structured and unstructured data.
• Experience Elasticsearch and Kibana for search/visualization.
• Experience with Git-based version control and notebooks.
• Ability to document solutions and brief stakeholders.
• Clear communication with multiple stakeholders.
• Ability to build reusable, tested data solutions.
• Academic or professional background in related fields.

Responsibilities

• Design, build, and maintain scalable data pipelines and infrastructure.
• Develop and optimize ETL/ELT workflows for diverse data sources.
• Build data integration across cloud, web, and on‑prem systems.
• Ensure data quality, security, and reliability across pipelines.
• Develop data processing solutions using Python and Bash in Linux.
• Construct and optimize complex queries across multiple data sources.
• Develop ingestion pipelines using Apache NiFi.
• Process and transform large-scale datasets from varied sources.
• Create reusable Python modules and validated workflows.
• Use Elasticsearch and Kibana for data visualization.
• Document technical solutions for technical and non-technical stakeholders.
• Communicate findings via reports, dashboards, and briefings.
• Collaborate across teams to support data-driven initiatives.
• Contribute to knowledge sharing by mentoring juniors.

Skills

Data engineering

ETL/ELT design

Python processing

SQL & relational DBs

Linux Bash scripting

Apache NiFi

Structured/unstructured data

Elasticsearch & Kibana

Git version control

Jupyter Notebooks

Documentation & briefings

Communication

Reusable data solutions

Math/CS background

Education

Math, statistics, physics, CS, or data science

Tools

Docker

Kubernetes

Tableau

Superset

Neo4j

Git

Apache NiFi

Job description

Job Description We are seeking a Data Scientist with strong data engineering capabilities to design, build, and maintain scalable data infrastructure and analytics solutions. This role focuses on developing robust data pipelines, enabling data-driven decision-making, and supporting machine learning and advanced analytics use cases across structured and unstructured data environments. The ideal candidate has strong programming skills, experience working across distributed data systems, and the ability to translate complex technical data problems into reusable, production-ready solutions for stakeholders.

Duties & Responsibilities

• Design, build, and maintain scalable data pipelines and infrastructure supporting analytics, reporting, and machine learning use cases.
• Develop and optimize ETL and ELT workflows for structured and unstructured data sources.
• Build and maintain data integration layers across cloud, web, and on-premise systems.
• Ensure data quality, consistency, security, and reliability across data pipelines and storage systems.
• Develop data processing solutions using Python, SQL, and Bash scripting in Linux environments.
• Construct and optimize complex queries across multiple data sources (e.g., PostgreSQL, MySQL, Neo4j, RDS).
• Develop and manage ingestion pipelines using tools such as Apache NiFi.
• Process and transform large-scale datasets from diverse structured and unstructured sources.
• Develop reusable, tested, and reproducible data workflows and Python-based modules.
• Use Elasticsearch and Kibana for search, indexing, and data visualization use cases.
• Document technical solutions, data pipelines, and methodologies for both technical and non-technical stakeholders.
• Communicate findings through written reports, dashboards, and oral briefings to stakeholders.
• Collaborate across multiple teams to support data-driven decision-making and analytics initiatives.
• Support knowledge sharing by explaining complex data concepts to junior team members. Required Skills Requirements:

Required Skills Requirements

• Strong experience in data engineering and data pipeline development, including ETL/ELT design and implementation.
• Proficiency in Python programming for data processing and automation.
• Strong experience with SQL and relational database systems.
• Experience working in Linux environments with advanced Bash scripting.
• Experience building and managing data pipelines using Apache NiFi or similar tools.
• Experience processing both structured and unstructured data sources.
• Experience working with Elasticsearch and Kibana.
• Experience using Git-based version control systems.
• Experience using Jupyter Notebooks for analysis and prototyping.
• Experience delivering technical results through documentation and stakeholder briefings.
• Strong communication skills and experience working with multiple stakeholders.
• Experience creating reusable, tested, and maintainable data solutions.
• Academic or professional background in math, statistics, physics, computer science, data science, or related fields.

Desired Skills Preferred Qualifications

• Experience with cloud platforms such as AWS and cloud-based data architecture.
• Experience with big data processing frameworks such as Apache Spark or Trino.
• Experience applying machine learning algorithms and NLP techniques.
• Experience with containerization technologies such as Docker or Kubernetes.
• Experience with data visualization tools such as Tableau, Kibana, or Apache Superset.
• Experience working with or designing machine learning workflows and models.
• Experience creating training materials or technical curriculum in data or scientific domains.
• Familiarity with data science MLOps or production ML workflows.

Additional Details What we offer

• Flexible time off
• Full medical coverage
• 401(k) with company match
• Referral bonuses
• Performance bonuses
• Life insurance and disability coverage
• Tuition and training reimbursement