DATA501: Data Intelligence Stack Owner at JerseySTEM Inc

, , United States -

Full Time

Start Date

Immediate

Expiry Date

01 Sep, 26

Salary

0.0

Posted On

03 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Architecture, Generative AI, Predictive Modeling, SQL, Python, Data Engineering, ETL/ELT, Prompt Engineering, Data Visualization, Cloud Data Warehousing, LLM Integration, Data Governance

Industry

Education Administration Programs

Description

About JerseySTEM All JerseySTEM roles are pro-bono (unpaid) positions.JerseySTEM is a mission-driven professional network of pro-bono contributors dedicated to improving access to STEM education and career pathways for underserved middle school girls in New Jersey. Members contribute their professional skills and leverage their networks in service of the organization’s gender-equity agenda.Membership is a minimum six-month commitment of approximately six flexible hours per week and includes a $100 refundable deposit, returned after six months of active membership. K–12 educators, retirees, veterans, interns, and students are exempt from the deposit. Overview This is a pro-bono volunteer position. JerseySTEM is seeking an AI-Forward Data Intelligence Stack Owner to lead the evolution of our data-to-intelligence pipeline. The scope of this role is strictly focused on the Intelligence Layer of the tech stack, transforming organizational data into automated insights You will architect a stack that leverages Generative AI, Automated Data Pipelines, and Predictive Modeling to provide real-time, actionable intelligence. You are the bridge between raw data silos and an AI-enhanced decision-making culture. Responsibilities AI-Centric Architecture: Design and oversee a data stack (Google BigQuery, Vertex AI, or similar) that prioritizes AI readiness and automated data flow. Generative Insights: Implement AI "Chat-with-your-Data" interfaces and LLM-driven summarization for stakeholders to interact with organizational data. Predictive Analytics: Move beyond historical reporting to build models that predict volunteer churn, student engagement trends, and fundraising opportunities. Automated Governance: Utilize AI tools to monitor data quality, flag anomalies, and ensure privacy compliance automatically. Orchestration: Integrate data from platforms (Salesforce, Jira, Google Workspace) using AI-friendly ETL/ELT processes that minimize manual intervention. Implementation: Directly configure and code/test the initial data pipelines, AI integrations, and reporting logic before scaling through the lean team." Team Leadership: Recruit and mentor a "Lean Data Team" focused on modern techniques like prompt engineering for data, automated visualization, and data engineering. Qualifications Applied AI for Analytics: Strong understanding of how to use LLMs specifically for data extraction, SQL generation, and automated synthesis Modern Data Stack Expertise: Experience with cloud data warehouses and AI-integrated BI tools (e.g., Looker’s Duet AI, Power BI Copilot). Data Engineering Mindset: Proficiency in SQL and Python, with an emphasis on building clean data for AI consumption. Visionary Thinking: Ability to replace spreadsheets with automated, intelligent solutions. Ability to translate AI-driven outputs simply to cross-functional partners. Comfortable working in a remote, distributed, member-driven environment. Minimum commitment of 6 hours per week. Preferred Qualifications Experience building data products, automated reporting agents. Previous experience in a leadership role within a tech-focused nonprofit or startup leading through shifts from legacy to automated data ecosystems. Familiarity with "AI-as-a-Service" platforms and low-code AI automation. Retired professionals or those seeking meaningful pro bono work are welcome. Key Outcomes The Intelligent Stack: A data environment where AI performs the first-layer of analysis. Self-Service Intelligence: Stakeholders can get answers to complex questions through automated agents rather than waiting for manual reports. Zero-Latency Insights: Implementing systems where key stakeholders receive automated proactive alerts and summaries via Chat/Email driven by data anomalies or trends Scalable Team: A high-performing member team trained in AI-forward data practices

Responsibilities

Lead the evolution of the data-to-intelligence pipeline by architecting a stack that leverages Generative AI and predictive modeling. The role involves designing AI-centric architectures and mentoring a lean data team to transform raw data into automated insights.