Senior Data Engineer
Location
United States
Posted
53 days ago
Salary
0
Seniority
Senior
Job Description
Senior Data Engineer
SunStrong Management, LLC
Job Summary The Data Engineer – Asset Management Analytics supports internal Asset Management and Asset Accounting customers by delivering AI-driven analytics products that support asset onboarding and improve portfolio health, asset performance, operational decision-making, and accounting readiness. This role partners with Asset Management, Asset Accounting, Finance/Investor Reporting, Risk, and Operations to translate business requirements into reliable data sets, repeatable calculations, and automation across both operational and financial reporting workflows. A core emphasis of the position is (1) asset onboarding—organizing and normalizing incoming data tapes and documents into internal systems and models with strong quality controls; (2) investor reporting—developing queries and logic for complex investor reports and waterfall calculations, then streamlining and automating those workflows using modern technology, including LLM-enabled document intelligence where appropriate; and (3) asset accounting support—enabling subledger accounting, reconciliations, and integrations into supported general ledger systems.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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