Senior Data Solutions Engineer - Finance Technology

About this role

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Skills you'll use

What you'll do

• PRIMARY FUNCTIONS & RESPONSIBILITIES
• Finance Technology Data Solutions
• Design, build, test, deploy, and support Finance Technology data solutions that serve Fund Accounting processes and operating requirements.
• Define and implement Fund Accounting-specific transformations, reconciliation and control logic, data-quality rules, integrations, and data products.
• Develop integrations with fund administrators, Fund Accounting systems, files, APIs, and other domain sources in partnership with the teams that own the relevant platforms.
• Develop Finance Technology data products and integrations required to support Fund Accounting processes, controls, reporting, analytics, and operational workflows.
• Use SQL Server, Databricks, Python, PySpark, APIs, and related technologies to deliver scalable solutions from design through production support.
• Data quality, reconciliation, and controls
• Build validation, reconciliation, exception-management, monitoring, and control capabilities as part of shared delivery with Fund Accounting, Data Governance, Data Product Management, and engineering partners.
• Build and support validation, reconciliation, monitoring, exception-management, and control capabilities aligned with agreed business, data-quality, and control requirements.

Who can apply

• Education: Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline. Equivalent relevant experience will be considered.
• Experience Required: 5 to 8 years of hands-on data engineering, data solutions, or financial technology experience, ideally within financial services, asset management, accounting, investment operations, or another controlled enterprise data environment.
• Must-have capabilities
• Advanced SQL Server skills, including complex queries, stored procedures, performance tuning, and troubleshooting.
• Hands-on Databricks experience, including notebooks, workflows/jobs, Spark concepts, Delta Lake patterns, and production support.
• Python and/or PySpark for transformation, validation, automation, and testing.
• Strong ETL/ELT, data modeling, integration, and curated-data-layer experience.
• Experience building domain-specific transformations, integrations, data products, validation, reconciliation, monitoring, and controls.
• Ability to own technical delivery while working effectively within a broader enterprise data and reporting operating model.
• Git, pull requests, automated testing, deployment pipelines, and disciplined software delivery.

About Ares Management

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