Data Analyst 2

Build the Data Infrastructure • Design, build, and maintain data pipelines ingesting from Salesforce, Dynamics, Platform.sh, Snowplow, and cloud vendor sources • Develop and optimize ETL/ELT workflows using Python and PySpark for large-scale batch and incremental processing • Build and maintain data models (fact/dimension tables) for customers, deals, margin, usage, and migration data • Partner with Analytics and BI teams to ensure pipeline outputs meet their modeling and reporting needs Ensure Data Reliability at Scale • Own data quality, validation, and monitoring across ingestion and transformation layers • Design for scalability, fault tolerance, and performance across growing data volumes • Build orchestration, alerting, and error-handling into pipelines to catch issues before they reach downstream consumers • Manage schema evolution, versioning, and backfills as source systems and business needs change Enable the Business Through Solid Data Foundations • Translate data requirements from Analytics, Finance, Product, and Engineering into robust pipeline and schema designs • Ensure data is accurate, timely, and well-documented so downstream teams can trust it without re-validating it themselves • Support pre- and post-launch data needs for ACCS and ACO by ensuring the right raw and modeled data is available and correct