Lead, Data Engineering & Analytics - TS/SCI Required

Overview

At LMI, we’re reimagining the path from insight to outcome at The New Speed of Possible™. With over 60 years of federal expertise and a strong innovation ecosystem, we accelerate mission success by delivering timely, high-impact solutions that help our customers adapt to evolving mission needs.    LMI is seeking a Technical Lead to support an Intelligence Community client. This position will be located in Washington, DC or Reston, VA. This role is a hands-on technical leader responsible for guiding a team that delivers modern data pipelines and advanced analytics in support of mission decision-making. 

 

LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.

 

Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

Responsibilities

Technical Leadership & Team Collaboration

• Lead and oversee a multidisciplinary team of data engineers and data scientists.  
• Collaborate with business/functional stakeholders to understand processes, define analytical requirements, and communicate results. 
• Mentor junior team members across data engineering and data science disciplines. 
• Build and maintain strong relationships with stakeholders to ensure alignment with organizational goals. 
• Manage delivery of projects, including timelines, deliverables, resources, and quality. 
• Provide technical and process consulting in support of mission outcomes.  

Data Engineering & Platform Modernization 

• Lead the modernization, maintenance, and scaling of data pipelines, data warehouses, and related infrastructure. 
• Contribute to the organization’s data engineering and advanced analytics strategy, roadmap, and data governance practices.

Advanced Analytics & Modeling

• Frame and scope analytical problems; integrate, consolidate, and analyze complex datasets. 
• Guide development and validation of models using machine learning, simulation, causal, rule-based, or statistical methods. 

Analytics Delivery & Communication

• Translate analytical results into dashboards, visualizations, and analytic narratives that support decision-making. 
• Provide timely analysis and reporting in a fast-paced, client-focused environment. 
• Advise non-technical stakeholders on interpreting and applying data products, dashboards, and reports.

Qualifications

Education:

• Bachelor’s degree in data science, mathematics, statistics, economics, computer science, engineering, or a related quantitative discipline is required; advanced degree preferred. 

Experience:

• 5-10 years of relevant experience, with at least 2 years leading data engineering or data science teams as a technical lead or task lead.
• Demonstrated experience delivering complex data pipelines and analytical projects in client-focused environments. 

Technical Skills:

• Proficiency in Python and SQL is required. Strong working knowledge of relational databases, including database optimization, schema design, and connecting analytic products to data sources. 
• Experience with designing, building, and maintaining ETL/ELT pipelines and data integration workflows in support of scalable analytics solutions. 
• Familiarity with core data science and analytics libraries in Python to support modeling, analysis, and feature engineering.  
• Experience building visualizations, dashboards, and lightweight analytic applications to communicate findings and drive business impact using modern platforms (e.g., Tableau, Streamlit, or similar tools). 
• Exposure to additional analytic, visualization, and programming tools (e.g., Qlik, Power BI, RShiny, Plotly, Java, R), demonstrating the ability to adapt across technologies. 
• Familiarity with data engineering and data science methods including data transformation, feature engineering, predictive analytics, and unstructured text analysis. 

Leadership and Interpersonal Skills:

• Strong written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. 
• Demonstrated ability