**What you’ll do:** Join our Finance Data Hub team to engineer the pipelines and data assets that power Commercial Finance Analytics and Core Finance domains. You’ll build scalable solutions in Snowflake and Azure Data Factory, support semantic models in Power BI, and ensure data is trusted, governed, and ready for advanced analytics and AI. This role blends hands-on engineering with an understanding of business context—helping Finance teams unlock insights that drive smarter decisions. "• Build & run data pipelines in ADF: develop parameterized pipelines, triggers, and orchestrations from source → bronze/silver → gold with operational logging and alerting. • Write excellent SQL in Snowflake: implement ELT transformations, window functions, SCD handling, and performance tuning (warehouses, materialized views, search optimization). • Model finance data assets: create and maintain facts/dimensions; align to medallion (bronze/silver/gold) standards and conforming dimensions. • Support the semantic layer in Power BI: help shape curated datasets, define/calibrate DAX measures, implement RLS, and manage deployment pipelines & refresh schedules. • Validate & reconcile data: build automated checks (Snowflake⇄Power BI), assist with UAT, and troubleshoot refreshes/gateways to maintain reliability and SLA compliance. • Engineer for security & compliance: implement RBAC/least privilege, masking, and RLS per policy; support SOX evidence capture for finance datasets. • Operate with CI/CD & Git: contribute to repeatable build/release practices across dev/QA/prod for SQL, pipelines, and semantic datasets. • Design with business context: partner with DF&I product owner and SMEs—translate goals (e.g., lower DSO) into requirements (e.g., global AR aging asset) and deliverables. • Contribute to standards/playbooks: apply and improve our data mesh and modeling standards; share reusable patterns via code reviews and documentation. • Enable AI readiness: capture lineage/metadata, enforce data quality, and structure measures so AI/advanced analytics can use finance assets responsibly. " **Qualifications:** Bachelor’s degree in Computer Science, Data/Information Systems, Engineering, Mathematics, or equivalent practical experience. "2–3 years professional data engineering experience (this is likely your second role). 2+ years hands‑on SQL development for analytical/ELT workloads. 1–2 years with a cloud data platform (Snowflake preferred), Azure Data Factory, and exposure to Power BI datasets/semantic models. " **Skills:** "• SQL mastery for analytics: CTEs, window functions, analytic aggregates, query profiling, and performance tuning in Snowflake. • Dimensional modeling: facts/dimensions, conformed dimensions, and SCD (Type 1/2) aligned to our gold layer standards. • Medallion & mesh basics: working knowledge of bronze/silver/gold and domain ownership/federated governance concepts; ability to apply standards in daily work. • ADF orchestration: linked services, parameterized datasets/activities, triggers, Key Vault integration, and robust error handling. • Power BI semantic support: dataset modeling, beginner to intermediate DAX, RLS, and deployment pipelines; familiarity with Tabular Editor/DAX Studio helpful. • Testing & DataOps: data validation & reconciliation, unit/integration tests for SQL/ELT, and Git based CI/CD participation. • Observability: monitor pipeline runs, warehouse usage, dataset refreshes, and gateway health; triage and prevent incidents. • Security & compliance: apply RBAC/least privilege, RLS, masking; awareness of SOX/privacy implications for finance data. • AI data readiness: capture lineage/metadata, uphold data quality thresholds, and structure measures for downstream AI/ML consumption within governance guidelines. " "- Business‑anchored thinking: can frame engineering tasks in terms of finance outcomes (e.g., working capital, DSO) - Curiosity for source systems: eager to learn ERP/subledger data and translate it into clean, governed assets. - Team play & communication: collaborates with DF&I product owner, Finance SMEs, architects, stewards, and platform/security; documents clearly and asks great questions. - Ownership & growth mindset: proactive in debugging, automating, and improving patterns; receptive to code reviews and standards."