Sr Enterprise Technical Lead

Job Description

Job Description

\n

NM2TECH is seeking a Senior Enterprise Technical Lead who is a hands-on expert in data analytics, visualization, and AI-driven solutions. This role requires a strong technical leader who actively contributes to development while guiding a team of analysts and data scientists.

\n

\n

The ideal candidate will have deep experience in Tableau, SAS, Python, and Artificial Intelligence / Machine Learning, and will play a key role in designing and delivering enterprise-scale analytics solutions. This is a working lead role, requiring both technical execution and team leadership. The ideal candidate will have an active BI or NACLC Public Trust security clearance, be a confident leader experienced in performing day-to-day tasks, a critical thinker and self-starter who takes ownership of assignments

\n

Previous consulting experience using an agile delivery methodology is a plus.

\n

Requirements :

\n

Security Clearance: Must possess active BI or NACLC Public Trust security clearance

\n

·      Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or related field.

\n

·      8+ years of hands-on experience in analytics, data science, or business intelligence.

\n

·      2–4 years of technical leadership experience in analytics or data science teams.

\n

·      Strong hands-on expertise in:

\n

o  Tableau – dashboard development and enterprise visualization

\n

o  SAS – statistical analysis and large-scale data processing

\n

o  Python – data analysis, automation, and machine learning

\n

·      Experience building AI / Machine Learning models for real-world business applications.

\n

·      Strong SQL and data warehousing knowledge.

\n

·      Experience working with large enterprise datasets and complex analytics environments.

\n

·      Experience in a matrix structure and communicating with technical and non-technical people. Ability to develop and deliver presentations that simplify complex solutions/insights for non-technical audience a must

\n

·      Excellent innovation, interpersonal, and communication skills required.

\n

Preferred Qualifications:

\n

·      Experience with cloud data platforms (AWS, Azure, or GCP).

\n

·      Familiarity with data engineering pipelines and ETL frameworks.

\n

·      Experience in integrating AI solutions into enterprise analytics platforms.

\n

·      Exposure to Agile development environments.

\n

Key Skills:

\n

·      Advanced Analytics & Data Science

\n

·      AI / Machine Learning Development

\n

·      Tableau Dashboard Engineering

\n

·      Statistical Modeling (SAS)

\n

·      Python Programming for Data Science

\n

·      Enterprise Data Architecture

\n

·      Technical Leadership & Mentoring

\n

·      Data Visualization & Storytelling

\n

Key Responsibilities: Lead the design, development, and deployment of enterprise analytics solutions to support strategic business decision-making.

\n

·      Serve as a hands-on technical lead, actively developing dashboards, analytics models, and data solutions.

\n

·      Build and maintain interactive dashboards and reporting solutions using Tableau to deliver actionable insights.

\n

·      Perform advanced data analysis, statistical modeling, and data processing using SAS and Python.

\n

·      Develop and implement AI and machine learning models for predictive analytics, forecasting, and business optimization.

\n

·      Write and optimize Python scripts and analytics workflows for data transformation, automation, and model development.

\n

·      Collaborate with business stakeholders, product teams, and data engineers to translate business requirements into scalable analytics solutions.

\n

·      Conduct code reviews and provide technical mentorship to analysts and data scientists.

\n

·      Ensure data quality, governance, and best practices in analytics development and reporting.

\n

·      Design and optimize data models, queries, and datasets to support high-performance analytics and visualization.

\n

·      Communicate complex analytical findings and recommendations to technical and non-technical stakeholders.