Build and maintain advanced data engineering solutions - spanning data ingestion, storage, processing, and publication - with a focus on enabling modern AI and analytics workflows.
Contribute to the design, development, and deployment of the enterprise data platform, ensuring it meets the scalability, performance, and security needs of the business.
Implement and optimize cloud-based data platforms with a focus on leveraging tools like Snowflake, Databricks, and big data ecosystem technologies to drive data transformation and analytics for AI enablement.
Partner with senior platform leadership to align development initiatives with the firm's strategic data management roadmap and enterprise architecture.
Evaluate and implement emerging technologies and tools to enhance the capabilities of the platform, ensuring the firm remains at the forefront of data innovation.
Contribute to data quality, observability, and monitoring frameworks that ensure platform reliability and trust in the data at scale.
Provide technical guidance and mentorship to junior engineers, helping elevate the broader team's capabilities and engineering standards.
Requirements
7+ years of hands-on platform or data engineering experience, with a demonstrable track record of owning and delivering production-grade systems. Financial or investment management background is strongly preferred.
Bachelor's degree is preferred, with a master's or equivalent experience in a relevant field being a plus.
Demonstrated experience and proficiency across the modern data stack in data engineering and ELT development, including designing and building robust, scalable data pipelines that handle large volumes of both structured and unstructured data.
Proven hands-on experience with technologies such as Java, Kafka, Spark, Snowflake, Airflow, Databricks, Azure, dbt and OpenShift.
Proficiency with Python and python-based data science libraries (e.g., scikit-learn, pandas, NumPy, PyTorch, or TensorFlow) and the ability to collaborate effectively in Python-centric data science environments.
Hands-on experience with Apache Airflow or similar orchestration and scheduling tools.
Proficiency in cloud deployment models and platforms like AWS, Azure, or Google Cloud.
Strong communication skills, with the ability to engage effectively with both technical and non-technical stakeholders.
Highly collaborative approach with the ability to work in dynamic, fast-paced environments.
Ability to identify and solve complex problems, leveraging emerging technologies to drive innovation and operational efficiency.
Leadership:
Ability to work independently while fostering a collaborative team environment.
Effective at communicating technical concepts - including data science workflows - and strategic priorities to a wide range of audiences.
Highly driven and results-oriented, with a passion for delivering high-impact solutions at the intersection of data engineering and applied data science.
Work model
Location: New York City; hybrid with a minimum of two days per week in office.
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Applicants must be authorized and have the right to work in the country where the role is located without the need for current or future sponsorship.
Compensation Details
Benefits
Vision insurancePerformance bonus
Additional Information
Neuberger is seeking a Senior Developer II to help architect and scale the firm's enterprise data platform. Specifically, this role sits at the intersection of AI, Data Science, and Enterprise Data Management. This is a high-visibility, hands-on engineering role at the heart of the firm's data-driven transformation, offering the opportunity to shape platform strategy, influence technical direction, and contribute meaningfully to research and data science enablement. This role is ideal for a highly motivated and technically proficient professional looking to contribute to the firm's data and analytics vision, while furthering their expertise in modern data technologies, cloud-based solutions, and applied data science methodologies.
Nb · New York, NY