Senior Data Solutions Engineer - Finance Technology

Role & responsibilities

Finance Technology Data Solutions

• Design, build, test, deploy, and support Finance Technology data solutions that serve Fund Accounting processes and operating requirements

-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.

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.

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.

Preferred candidate profile

Education: Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline. Equivalent relevant experience will be considered.

Experience Required: 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.