Data Scientist & Engineer (Security Clearance) ML Pipelines

BOAB Ventures

McLean (VA)

In loco

USD 120.000 - 180.000

Tempo pieno

10 ore fa
Candidati tra i primi

Una candidatura completa in un minuto

Curriculum e lettera di presentazione personalizzati, pronti da inviare.

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Vantaggi offerti da questo lavoro

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

Competenze

  • Strong experience in data engineering and data pipeline development (ETL/ELT).
  • Proficiency in Python programming for data processing and automation.
  • Strong SQL and relational database experience.
  • Experience with Linux environments and Bash scripting.
  • Experience building data pipelines with Apache NiFi or similar tools.
  • Experience processing structured and unstructured data.
  • Experience with Elasticsearch and Kibana.
  • Experience using Git for version control.
  • Experience using Jupyter Notebooks for analysis/prototyping.
  • Ability to document solutions and brief stakeholders.
  • Strong communication skills for multi-stakeholder environments.
  • Background in math/statistics/computer science/data science or related fields.

Mansioni

  • Design, build, and maintain scalable data pipelines and infrastructure for analytics, reporting, and ML use cases.
  • Develop and optimize ETL/ELT workflows for diverse data sources.
  • Build and manage data integration across cloud, web, and on-prem systems.
  • Ensure data quality, security, and reliability across pipelines and storage.
  • Develop data processing solutions using Python, SQL, and Bash in Linux environments.
  • Construct and optimize complex queries across multiple sources (PostgreSQL, MySQL, Neo4j, RDS).
  • Develop and manage ingestion pipelines using Apache NiFi and similar tools.
  • Process and transform large-scale datasets from varied sources.
  • Create reusable, tested Python-based data workflows/modules.
  • Use Elasticsearch and Kibana for search, indexing, visualization.
  • Document technical solutions and pipelines for technical and non-technical audiences.
  • Communicate findings via reports, dashboards, and briefings to stakeholders.
  • Collaborate across teams to enable data-driven decision-making.
  • Mentor juniors and share knowledge across the team.

Conoscenze

Data engineering
Python
SQL
Linux/Bash
Apache NiFi
Elasticsearch
Kibana
Git
Jupyter Notebooks
Data pipelines
Communication

Formazione

Math/Statistics/CS/DS background

Strumenti

Apache NiFi
Elasticsearch
Kibana
Git
Jupyter
Tableau
Docker
Kubernetes
Apache Spark
Trino

Descrizione del lavoro

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
  • 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
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