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Senior Data Solutions Engineer - Finance Technology

senior · Technology / Software Development

Over the last 20 years, Ares’ success has been driven by our people and our culture. Today, our team is guided by our core values – Collaborative, Responsible, Entrepreneurial, Self-Aware, Trustworthy – and our purpose to be a catalyst for shared prosperity and a better future. Through our recruitment, career development and employee-focused programming, we are committed to fostering a welcoming and inclusive work environment where high-performance talent of diverse backgrounds, experiences, and perspectives can build careers within this exciting and growing industry.

Job Description

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.
• Support investigation and resolution of data issues through technical evidence, root-cause analysis, and sustainable remediation.
• Maintain appropriate lineage, documentation, test evidence, observability, and operational support procedures for Finance Technology-owned solutions.

Enterprise platform alignment and partnership

• Leverage enterprise platforms, pipelines, governed data layers, shared engineering capabilities and patterns, and reusable enterprise data products.
• Partner with Enterprise Data Engineering on Fund Accounting requirements, source data, enterprise integrations, domain transformations, data-quality expectations, controls, and consumption needs.
• Contribute Fund Accounting domain expertise to enterprise data products and initiatives so enterprise data can be reliably applied to Fund Accounting use cases.
• Align Finance Technology solutions with enterprise-wide engineering standards while maintaining ownership of domain-specific solutions and delivery outcomes.

Data Products & Downstream Consumption

• Build Finance Technology data products that support Fund Accounting operational processes, dashboards, reports, analytics, extracts, downstream applications, and other consumption needs.
• Design scalable data models, transformations, controls, reconciliations, and interfaces that enable consistent downstream consumption.
• Deliver governed datasets, technical definitions, lineage, refresh processes, and quality controls required for reporting and other downstream use cases.
• Partner with Centralized Reporting / BI by providing trusted data products and technical foundations required for scalable reporting solutions.
• Support the ongoing operation, monitoring, enhancement, and lifecycle management of Finance Technology data products.

Business partnership and engineering discipline

• Collaborate across Finance Technology, Fund Accounting, Enterprise Data Engineering, Data Governance, Data Product Management, Centralized Reporting / BI, application teams, and delivery partners throughout the delivery lifecycle.
• Apply sound practices for data modeling, performance, security, access control, retention, testing, deployment, and environment management.
• Use Git, peer review, automated testing, CI/CD, controlled releases, and documented rollback and support procedures.
• Communicate technical issues in clear business language and make practical trade-offs while maintaining accountability for Finance Technology outcomes.

QUALIFICATIONS

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.

Strongly preferred

• Azure Data Factory, Data Lake Storage, Azure DevOps, Microsoft Fabric, APIs, JSON, and secure file-transfer patterns.
• Experience with Fund Accounting systems, fund administrator data, financial balances, transactions, capital activity, or multi-system reconciliations.
• Working knowledge of metadata, lineage, catalog, master/reference data, governance, controls, and auditability concepts.
• Experience structuring governed data for dashboards, reports, extracts, downstream applications, operational workflows, and analytics.

How success will be measured

• Fund Accounting data is reliably transformed, reconciled, controlled, and applied to operational and consumption use cases.
• Finance Technology data products are trusted and usable across dashboards, reports, extracts, downstream applications, workflows, and analytics.
• Enterprise capabilities are reused where appropriate without weakening Finance Technology ownership of domain-specific solutions and outcomes.
• Domain-specific integrations, transformations, reconciliations, and controls reduce manual work and recurring data issues.
• Clear accountability and strong handoffs are established across the broader data and reporting operating model.

Reporting Relationships

There is no set deadline to apply for this job opportunity. Applications will be accepted on an ongoing basis until the search is no longer active.