Analyst, Information Systems

Your responsibilities will include Designs and builds analytical data models prioritized by D&A (with support from the Analytics Lead and Data Product Owner); Develops, tests, and maintains transformation logic (SQL/Dataform) across Silver and Gold layers; Performs and documents data validation tests (quality checks, reconciliation, metric validation); Supports Data Product Owners (BI Teams and Data Science Teams) during UAT and metric validation; Collaborates with Data Engineers to ensure reliable upstream data ingestion and proper modeling grain; Supports analytical model deployment through pull requests and downstream validation. Supports and trains Analytics/BI teams on new datasets, KPI definitions, and semantic models (KT); Hands-on experience designing and maintaining analytical data models in BigQuery (Silver/Gold layers) Strong proficiency in SQL (BigQuery or similar), including joins, window functions, aggregations, CTEs, and performance-aware query design; Fact and dimension tables Proper grain definition Surrogate keys Basic Slowly Changing Dimensions (SCD) concepts Null checks Duplicate detection Referential integrity validation Reconciliation logic Experience supporting UAT with BI teams and business stakeholders Working knowledge of Cloud Composer (Airflow) and data dependency orchestration concepts Understanding of how analytical modeling decisions impact Tableau (or similar BI tools) performance Proficiency with Git-based workflows, pull requests, and SQL code reviews IAM roles Service accounts Dataset-level access control Basic cost governance awareness Ability to document transformation logic, KPI definitions, and business rules following enterprise standards Exposure to Dataflow and Pub/Sub, understanding upstream ingestion patterns Experience working with SAP (ECC, BW, or S/4) or enterprise ERP systems Experience with Dataform or structured SQL transformation frameworks Partitioning strategies Clustering Aggregated tables Cost optimization techniques Exposure to data governance and metric standardization practices Experience working in large-scale enterprise environments Exposure to data observability concepts (freshness monitoring, reconciliation dashboards) Experience participating in Knowledge Transfer (KT) activities and supporting sustain teams Strong stakeholder communication skills Professional working proficiency in English (preferred) Exposure to GenAI-assisted development tools for SQL or documentation automation