Operations and Data Quality Analyst

Operations and Data Quality Analyst Job Description We are looking for an Operations and Data Quality Analyst to join our Data Engineering team. As an Operations and Data Quality Analyst, you will help lay the foundation for a Data Operations pillar within Data Engineering — developing a deep understanding of production job dependencies across our Snowflake data platform, building operational run books, and establishing the monitoring and incident management practices that keep production data reliable.

You will own the Monte Carlo Data Observability Platform, building and maturing a comprehensive data quality suite that proactively detects issues before they reach downstream consumers, and will serve as the data quality owner for the team. As part of a high-performing team working on mission-critical data with visibility across the organization, you will develop critical insight into the company and support every function of the business, taking ownership of data quality and treating data as a product.

Responsibilities •         Develop and maintain a comprehensive understanding of production job dependencies, data flows, and scheduling across the Snowflake data platform. •         Build, document, and maintain operational run books covering routine processes, job recovery procedures, escalation paths, and incident response.

•         Monitor production data pipelines and batch processes; triage failures, coordinate resolution, and communicate status to stakeholders — helping establish the operational standards, SLAs, and incident management processes for a new Data Operations pillar. •         Own the Monte Carlo Data Observability Platform — including configuration, administration, and adoption — and design and build a comprehensive data quality suite with monitors for freshness, volume, schema changes, and field-level anomalies across critical data domains.

•         Define data quality rules, thresholds, and alerting workflows; establish data quality SLAs and scorecards, and report on data health to Data Engineering leadership and business stakeholders. •         Perform data validation at both the intake (Snowflake ingestion) and output (downstream delivery) stages to ensure accuracy, completeness, and integrity.

•         Develop and execute data quality checks, reconciliation routines, and exception reports to identify and resolve discrepancies; serve as the data quality owner for assigned data domains, documenting findings and partnering with Data Engineering to resolve root causes. •         Validate bordereau data received from MGAs/Program Administrators against expected schemas, field requirements, and business rules.

•         Design, build, and maintain multi-layered dashboards, KPI reports, and ad-hoc analyses to support Finance, Operations, and Leadership; perform quantitative and statistical analysis to surface trends, anomalies, and business insights. •         Contribute to monthly, quarterly, and annual financial close processes by validating data and producing supporting schedules.

•         Partner closely with Data Engineers to define, test, and validate data pipelines — providing business context and analytical perspective that engineers may not have. •         Participate in requirements gathering and UAT for new data integrations, system enhancements, and reporting solutions; understand system capabilities across Snowflake and reporting platforms to design queries and outputs optimized for performan