Senior Data Solutions Engineer - Finance Technology

Finance Technology Data Solutions

• Design, build, test, deploy, and support data solutions serving Fund Accounting processes and operating requirements.
• Define Fund Accounting transformations, reconciliations, controls, data-quality rules, integrations, and data products.
• Develop integrations with fund administrators, Fund Accounting systems, files, APIs, and other domain sources.
• Build data products supporting Fund Accounting processes, controls, reporting, analytics, and operational workflows.
• Use SQL Server, Databricks, Python, PySpark, APIs, and related technologies through production support.

Data Quality and Controls

• Build validation, reconciliation, exception-management, monitoring, and control capabilities.
• Investigate data issues through technical evidence and root-cause analysis, then implement sustainable remediation.
• Maintain lineage, documentation, test evidence, observability, and operational support procedures.

Enterprise Partnership

• Leverage enterprise platforms, pipelines, governed data layers, shared engineering patterns, and reusable data products.
• Partner with Enterprise Data Engineering on Fund Accounting requirements, source data, integrations, transformations, controls, and consumption needs.
• Align Finance Technology solutions with enterprise engineering standards while retaining ownership of domain-specific outcomes.

Data Products and Consumption

• Build data products for dashboards, reports, analytics, extracts, downstream applications, and operational workflows.
• Design scalable data models, transformations, controls, reconciliations, and interfaces for consistent downstream consumption.
• Deliver governed datasets, technical definitions, lineage, refresh processes, and quality controls.
• Support the operation, monitoring, enhancement, and lifecycle management of Finance Technology data products.

Engineering Discipline

• Collaborate across Finance Technology, Fund Accounting, data engineering, governance, reporting, application, and delivery teams.
• Apply 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.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline, or equivalent relevant experience.
• 5 to 8 years of hands-on data engineering, data solutions, or financial technology experience.
• Experience should ideally be within financial services, asset management, accounting, investment operations, or a controlled enterprise data environment.

Must-Have Capabilities

• Advanced SQL Server skills, including complex queries, stored procedures, performance tuning, and troubleshooting.
• Hands-on Databricks experience with notebooks, workflows or jobs, Spark concepts, Delta Lake patterns, and production support.
• Python or PySpark experience for transformation, validation, automation, and testing.
• Strong ETL/ELT, data modeling, integration, and curated-data-layer experience.
• Experience with domain-specific transformations, integrations, data products, validation, reconciliation, monitoring, and controls.
• Experience with 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.
• Fund Accounting systems, fund administrator data, financial balances, transactions, capital activity, or multi-system reconciliations.
• Metadata, lineage, catalog, master or reference data, governance, controls, and auditability concepts.
• Structuring governed data for dashboards, reports, extracts, downstream applications, operational workflows, and analytics.

Reporting Relationship