Lead, Analytics & Data Engineering - TS/SCI Required

Responsibilities

• 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.
• 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.
• 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.
• 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

• Bachelor’s degree in data science, mathematics, statistics, economics, computer science, engineering, or a related quantitative discipline is required; advanced degree preferred.
• 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.
• 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.
• Strong written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Demonstrated ability to mentor and develop junior team members across data engineering and data science disciplines.
• Strong stakeholder management skills, including the ability to build trusted relationships and balance modernization efforts with stakeholder-facing delivery.
• Ability to work in a fast-paced, solutions-oriented environment while delivering high-quality products.
• Strong analytical, problem-solving, and organizational skills with a focus on practical outcomes and mission impact.