> Markdown version of [/jobs/ext/4621067-data-engineer](https://www.wearedevelopers.com/jobs/ext/4621067-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer

- **Company:** KINECT, LLC
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $130,000.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Information Systems, Databases, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Normalization, Data Systems, Data Warehousing, Python (Programming Language), OpenShift, Operational Databases, Systems Development Life Cycle, SQL Databases, Unstructured Data, Workflow Management Systems, Data Processing, Sql Optimization, Snowflake, Apache Spark, Git, Pandas, Containerization, Information Technology, Apache Kafka, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-new-york-ny--c8fbc22b-10cf-41a9-9527-b057e6c420e2

## About the Role

Investment Management, Operational Audit, Engineering, Financial Services, Data Modeling, Data Analysis, Database Extract Transform and Load (ETL), Unstructured Data, Scalable System Development, Structured Data, The ideal candidate has strong data engineering experience within financial services and understands the challenges of working with operationally driven, non-standard investment data., 7+ years of professional data engineering experience building and supporting production data pipelines.
*
Financial markets data experience; private markets experience is strongly preferred.
*
Advanced SQL skills and experience designing dimensional data models.
*
Strong Python development skills and experience with data-processing frameworks such as Spark, Pandas, or Polars.
*
Experience with ETL/ELT orchestration tools such as Apache Airflow.
*
Hands-on experience with Snowflake and AWS and/or Azure.
*
Familiarity with dbt and version-controlled data transformation workflows.
*
Experience with Git, CI/CD, testing, and containerization.
*
Demonstrated experience using AI-assisted development tools and workflows.
*
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.

Experience with Kafka, data contracts, schema evolution, OpenShift, and private markets data normalization is preferred.

Technology Environment: SQL, Python, Snowflake, Apache Airflow, Spark, Pandas/Polars, dbt, AWS/Azure, Git, CI/CD, containerization, Kafka, and OpenShift., Applicants must be authorized to work in the United States without current or future employer sponsorship.

## Description

Our client, a leading global investment management firm, is seeking a Senior Data Engineer to design and scale the data infrastructure supporting its Private Markets business. This role will work closely with fund administration, operations, analytics, and technology teams to model, integrate, govern, and optimize complex investment data across multiple administrators, fund structures, and jurisdictions., Design and optimize data models supporting analytics, reporting, and operational workloads.
*
Build and maintain scalable ETL/ELT pipelines that ingest and normalize structured and unstructured data from multiple sources.
*
Translate private markets workflows such as capital calls, distributions, and co-investment activity into well-governed, reusable datasets.
*
Implement data quality controls, monitoring, alerting, and validation processes.
*
Partner with business users and development teams to define requirements and deliver accessible, reliable data solutions.
*
Improve query performance, pipeline throughput, and data platform efficiency.
*
Support data governance initiatives including documentation, lineage, cataloging, access controls, and golden-record ownership.
*
Leverage AI-assisted tools to improve development efficiency and data-processing workflows.