Senior Data Engineer

Job Summary

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.

Qualifications

• 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).
• CFA (Chartered Financial Analyst) candidacy/charterholder preferred, or comparable credentials/experience (e.g., FRM, CAIA, MBA with finance focus).
• 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.
• Experience supporting investor reporting and/or finance analytics, including complex recurring reports and waterfall/distribution calculations (preferred).
• Experience supporting asset accounting, subledger accounting, and GL integrations, including reconciliations, journal-entry support, and controls for accounting data pipelines (preferred).
• 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 (preferred).
• 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.
• Strong communication and stakeholder management skills; ability to translate ambiguous requirements into structured deliverables and clear exception reporting.
• Familiarity with Git, orchestration/workflow tools (e.g., Airflow), and responsible AI practices (privacy, security, PII handling, model risk) is a plus.

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