Quantitative Developer

CONTRACT ROLEThe Senior Quant Data Engineer designs, builds, and optimizes high-performance data pipelines and quantitative models supporting structured finance, asset-backed securities (ABS), and mortgage portfolio analytics.Core ResponsibilitiesDesign and implement Python-first data pipelines using Polars, Spark, and Pandas to ingest, normalize, and reconcile daily and monthly feeds from servicers across whole loans, ABS, HELOCs, RVs, student loans, and esotericsArchitect and own end-to-end pipeline design, including edge case handling, failure recovery, and performance tuning at 100K+ loan scaleLead quantitative reconciliation workflows — building tie-out frameworks that compare current vs. target LMS data models across complex structured finance instrumentsDesign reconciliation logic and sign-off criteria with full ownership of data accuracy under production parallel conditionsBuild and maintain Snowflake-based data models to support portfolio analytics, reporting, and AI-native dashboard generation across 300+ diverse dealsOptimize warehouse compute/storage and implement scalable dimensional and analytical data modeling patternsDevelop automated data quality, anomaly detection, and monitoring frameworks to ensure integrity of servicer data at scaleCollaborate with migration lead and full-stack developer to map LMS data model, execute migration engine build, and support production cutoverContribute to the AI-native dashboard layer — building Python/Plotly-Dash views and Snowflake-backed views replacing hand-crafted Tableau dashboardsEnsure data governance — metadata management, lineage tracking, and access controls.Required ExperienceYears of Experience: 7–10 years designing and maintaining data platforms in financial services or quantitative financeStructured Finance: Hands-on experience with ABS, whole loans, HELOCs, mortgage pools, student loans, or esotericsReconciliation: Proven ownership of high-volume reconciliation pipelines (100K+ records) in production environmentsData Normalization: Multi-servicer data normalization and loan-level data validationDelivery: Experience delivering data solutions under hard production deadlines with executive visibilityRequired Skills & TechnologyPython: Expert – Polars, Spark, Pandas for high-performance data processing and quantitative workflowsSnowflake: Advanced SQL, data modeling (star, snowflake, data vault), compute/storage optimizationReconciliation: Tie-out framework design — LMS migration validation, current vs. target model comparisonQuant Data Modeling: Cashflow logic, servicer tape normalization, collateral-level aggregationData Quality: Automated validation suites, anomaly detection, production monitoringAI-Native Development: Cursor / MCP agent workflows, AI-augmented pipeline deliveryAzure Data Factory: Orchestration and enterprise ETL workflow managementPlotly/Dash: Python-native analytics dashboards for institutional stakeholders (preferred)Communication: Ability to communicate quantitative logic to non-technical stakeholdersVersion Control: CI/CD integration for data pipeline deploymentsToolsPython · Snowflake · Polars · Spark · Pandas · Git · Cursor AI / MCP · Azure Data Factory · Plotly/Dash · Tableau (familiarity)Preferred CertificationsSnowflake SnowPro Core or Advanced: Data EngineerAWS Data Analytics SpecialtyCFA or FRM (or equivalent quant finance credential)Azure Data Engineer AssociatePreferred Experience & SkillsTechnical: Real-time or near-real-time streaming for daily servicer feed ingestion; ML/AI data pipeline integration; advanced quantitative modeling (prepayment, default, loss severity models)Education: Bachelor’s or Master’s in Quantitative Finance, Financial Engineering, Applied Mathematics, Statistics, Computer Science, or related fieldDelivery & Work StyleAgile or hybrid delivery modelsComfortable in embedded pod structures working directly alongside client engineering teams