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# Developer I - Data Engineer

- **Company:** Marketing & Technology Group Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $130,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Information Systems, Continuous Integration, Information Engineering, Data Files, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Python (Programming Language), OpenShift, Operational Databases, Cloud Services, Software Construction, Data Streaming, Unstructured Data, Workflow Management Systems, Sql Optimization, Large Language Models, Snowflake, Apache Spark, Git, Pandas, Containerization, Information Technology, Data Lineage, Star Schema, Integration Frameworks, Apache Kafka, Data Pipelines
- **Published:** July 17, 2026
- **Apply:** https://www.dice.com/job-detail/9d9f2a63-f623-4c79-9626-837b54e1e0e8

## About the Role

* Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
 * 7+ years of professional experience in data engineering, building and maintaining production data pipelines.
 * Financial markets data domain experience required with Private Markets data experience a plus
 * Demonstrated fluency with AI tools - co-pilot tools, LLM-assisted development, or AI-augmented data workflows
 * Advanced SQL skills and experience designing dimensional data models (star schema, snowflake schema).
 * Proficiency in Python and experience with data processing frameworks such as Apache Spark, Pandas, or Polars.
 * Hands-on experience with orchestration tools such as Apache Airflow
 * Experience with Snowflake and cloud services (AWS or Azure).
 * Familiarity with data transformation tools like dbt and version-controlled analytics workflows.
 * Solid understanding of software engineering best practices including Git, CI/CD, testing, and containerization.

Preferred Qualifications

 * Experience with streaming data architecture using Kafka
 * Experience implementing data contracts and schema evolution strategies.
 * Experience with OpenShift platform
 * Experience normalizing data across similar data sets
 * Experience private markets data context

Work model

Location: New York City; hybrid with a minimum of two days per week in office., 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.

## Description

Neuberger's Private Markets Technology group is seeking a Senior Data Engineer to design and scale the data infrastructure supporting our private equity and private debt strategies. This role sits at the intersection of data engineering and investment operations - you will work directly with fund administration, operations, and analytics teams to model, move, and govern data across a complex, multi-administrator environment spanning multiple fund structures and jurisdictions. Our data landscape is operationally driven and non-standard by nature - sourced from GP notices, fund administrators, data rooms, and bespoke operational workflows rather than exchange feeds or market data vendors. We need someone who understands that distinction and can build for it., * Develop and optimize data models in the data warehouse for analytics, reporting, and operational workloads - translating private markets workflows such as capital calls, distributions, and co-investment closings into well-governed, reusable datasets

 * Design, build, and maintain scalable ETL/ELT data pipelines that ingest and normalize large volumes of structured and unstructured data from multiple fund administrators with inconsistent booking conventions across diverse fund structures and jurisdictions
 * Implement data quality checks, monitoring, and alerting to ensure the reliability and accuracy of data across the platform.
 * Collaborate with users and development teams to understand data requirements and deliver well-modeled, accessible datasets.
 * Optimize query performance, pipeline throughput, and storage costs across the data platform.
 * Contribute to data governance practices including documentation, lineage tracking, cataloging, and access controls - with a focus on golden record ownership and how upstream data quality propagates through downstream investment and reporting systems
 * Leverage AI to drive efficient coding and process design


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