Data Engineer - New York Jobs CEO Council

JPMorgan Chase & Co.•New York, NY

1d•$137,750 - $185,000•Onsite

About The Position

This role is central to building the technical foundation that will power the Jobs Council's next generation of impact measurement - designing, building, and maintaining the data pipelines, schemas, and medallion architecture that connect fragmented data systems across programs, employer partnerships, and student engagement into a unified, analytics ready infrastructure. The Data Engineer will serve as the organization's internal technical lead for data integration and platform stewardship, reporting to the Senior Director for Data Strategy and working alongside the Data Analyst to ensure the Jobs Council's data infrastructure can support real-time reporting, predictive modeling, and AI-enabled insights at scale. This is an opportunity for an engineer who wants to build something from the ground up and see the direct impact of that work in how a mission-driven organization understands and advances workforce equity in New York City. The New York Jobs CEO Council fosters collaboration among business, education, and community leaders to prepare New Yorkers in diverse, low-income communities for the future of work and meet employer needs. The future of New York City's economy and workforce requires breaking down industry and educational silos and opening the door to local talent. We are guided by our goal to place 100,000 low-income New Yorkers from underserved and underrepresented communities into career-pathway, living wage jobs by 2030, with 25,000 of those hires coming from CUNY.

Requirements

5+ years of hands-on data engineering experience in production environments
Expertise in ETL/ELT pipeline design, data modeling, and cloud-native platforms
Proficiency in Python, SQL, and API/webhook-based integration patterns
Experience with relational databases, CRM systems (e.g., Salesforce), and modern lakehouse/medallion architectures
Track record of building automated reporting infrastructure and reusable data templates
Analytical mindset with excellent problem-solving abilities
Cross-Disciplinary Background: Experience that spans both data science and data engineering — you understand how data gets used downstream, not just how it gets stored.
Comfort with Broad Ownership: You’ve thrived in small or scaling organizations where wearing multiple hats was the norm — and you’ve sought out that kind of ownership and variety rather than resisted it.
Building Experience : You’ve built data infrastructure from the ground up — standing up tables, schemas, and pipelines in environments where the structure didn’t yet exist.
Strong Communicator : You’re comfortable as the technical expert in the room — asking the right questions and translating complex data concepts for leadership, vendors, and analysts alike.
Self-Directed : You’re able to work autonomously, prioritize effectively, and drive your work forward — you don’t wait for structure to exist before you create it.

Responsibilities

Design, build, and maintain automated ETL/ELT pipelines across various platforms (Db2, Salesforce, event platforms, Handshake, SurveyMonkey, 8x8, and SFTP partner feeds)
Design the database tables and schemas for analytical flexibility and long-term reuse.
Own and maintain the bronze/silver/gold layered architecture that supports advanced reporting, predictive models, and AI-enabled insights.
Implement governance controls, data quality checkpoints, role-based access, and documentation across the platform.
Execute continuous improvements to the data model as organizational needs evolve.
Serve as the internal technical lead and contribute in strategic data integration discussions to ensure data model sustainability, governance, and longevity.
Support the build-out of purpose-built dashboards for the Engagement team, Partnerships team, leadership, and external audiences. Partner with the Data Analyst to ensure the data model allows for KPI delivery.
Support the implement of automated reporting by maintaining the data model and developing target specific tables/views to allow for seamless visualization tools.
Maintain an up-to-date data dictionary and table/schema directory to allow for Data Analysts to self-service complex reporting.
Structure the data model in a way that can be utilized for various AI/ML tools (chatbots, reporting/spreadsheet generation, etc.)