Senior Data Engineer

SunStrong Management is seeking a Senior Data Engineer to support internal Asset Management and Asset Accounting customers by delivering AI-driven analytics products. The role involves partnering with various departments to translate business requirements into reliable data sets and automation across reporting workflows, focusing on asset onboarding, investor reporting, and asset accounting support.

Responsibilities:

• Support asset onboarding end-to-end: understand and parse data tapes to define/refine portfolio composition, map fields to internal models, and reconcile inconsistencies across sources
• Gather, organize, inventory, and load source documents and data into destination internal systems with clear traceability and audit-ready documentation
• Normalize incoming data (units, naming conventions, identifiers, hierarchies, dates) to work with internal models; identify gaps, exceptions, and remediation needs
• Validate data and documents for quality and completeness using automated checks and reconciliation (schema/constraints, cross-field validation, duplicate detection, tie-outs to source totals) and targeted manual review
• Partner with Finance/Investor Reporting to understand investor report requirements and develop robust SQL/Python queries that produce accurate, repeatable, and auditable data sets for complex recurring reports
• Develop and maintain calculation logic for investor reporting and distribution waterfalls (cash flows, allocations, fees, reserves, performance metrics) with strong controls, testing, and variance analysis
• Partner with Asset Accounting and Finance to define and maintain accounting-ready data sets, event-to-accounting mappings, and controls that support period-end close, reconciliations, and auditability
• Support subledger accounting workflows and integrations into supported GL systems by developing data transformations, interfaces, tie-outs, and exception reporting for journal entries, balances, and account activity
• Automate and streamline onboarding and investor reporting workflows by building parameterized pipelines, standardized datasets, templated outputs, and monitoring/alerts to reduce cycle time and manual effort
• Leverage LLMs to accelerate development and unlock value from large document sets (e.g., extraction, classification, summarization, grounded Q&A/search) to support onboarding and reporting, with appropriate governance and human review
• Deliver stakeholder-ready outputs through Power BI dashboards and operational reporting (progress, completeness, defect rates, variances, and SLA performance)
• Collaborate with Data Engineering/IT to improve data quality, lineage, and access controls across Snowflake and PostgreSQL; deploy solutions reliably and securely

Requirements:

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, Economics, Finance, or a related quantitative field
• 5+ years of experience in data science/analytics in a financial services environment (e.g., asset management, banking, insurance, fintech, credit/loan portfolios, structured finance)
• Strong Python and SQL skills, with experience building production-quality data pipelines, validation checks, and repeatable transformations
• Experience working with Snowflake and PostgreSQL, and delivering investor- and stakeholder-ready outputs through Power BI
• Strong communication and stakeholder management skills; ability to translate ambiguous requirements into structured deliverables and clear exception reporting
• CFA (Chartered Financial Analyst) candidacy/charterholder preferred, or comparable credentials/experience (e.g., FRM, CAIA, MBA with finance focus)
• Experience supporting investor reporting and/or finance analytics, including complex recurring reports and waterfall/distribution calculations
• Experience supporting asset accounting, subledger accounting, and GL integrations, including reconciliations, journal-entry support, and controls for accounting data pipelines
• Familiarity with supported GL systems and accounting data structures (e.g., chart of accounts, trial balance, journal entries, subledger-to-GL tie-outs) is strongly preferred
• Experience with asset onboarding, data tape ingestion, and reconciling third-party data and documents into internal systems
• Hands-on experience applying LLMs to enterprise data and large document corpora (e.g., extraction/classification/summarization and grounded retrieval such as RAG) in a way that is accurate, traceable, and secure
• Familiarity with Git, orchestration/workflow tools (e.g., Airflow), and responsible AI practices (privacy, security, PII handling, model risk) is a plus