Data Engineer II - IT

About the Role

Join the Finance Data Hub team to engineer pipelines and data assets that power Commercial Finance Analytics and Core Finance domains. The role focuses on building scalable solutions in Snowflake and Azure Data Factory, supporting semantic models in Power BI, and ensuring data is trusted, governed, and ready for advanced analytics and AI. You will design end-to-end data flows from source systems through bronze, silver, and gold layers, write performant ELT SQL in Snowflake, and maintain finance fact and dimension tables. In addition, you will help shape Power BI curated datasets, define DAX measures, implement row-level security, and manage refresh pipelines while automating data validation and supporting UAT. Security and compliance are critical; you will apply RBAC, least-privilege, masking, and SOX evidence capture, and work with CI/CD and Git to deliver repeatable builds across development, QA, and production environments. Collaboration with the DF&I product owner and finance SMEs will translate business goals such as lowering DSO into concrete data requirements. • Develop parameterized Azure Data Factory pipelines with logging, alerting, and orchestration across bronze, silver, and gold layers. • Write performant ELT SQL transformations in Snowflake, including window functions, SCD handling, and performance tuning. • Design and maintain finance fact and dimension tables aligned to medallion architecture. • Support Power BI semantic models by curating datasets, defining DAX measures, implementing row-level security, and managing refresh pipelines. • Build automated data validation checks between Snowflake and Power BI and assist with user acceptance testing. • Implement security controls such as RBAC, least-privilege access, data masking, and RLS, and provide SOX compliance evidence. • Deploy CI/CD workflows with Git for SQL scripts, ADF pipelines, and Power BI assets across dev, QA, and prod environments. • Translate finance business objectives (e.g., lower DSO) into concrete data requirements and deliverables. • Contribute to data mesh standards, create reusable patterns, and document best practices. • Capture data lineage and enforce quality thresholds to enable AI/ML consumption of finance assets.

Key Responsibilities

• ▸adf pipelines
• ▸elt sql
• ▸snowflake
• ▸power bi
• ▸ci/cd
• ▸data security

What You Bring

The ideal candidate holds a bachelor’s degree in a quantitative field or equivalent experience, has 2-3 years of data engineering practice, strong SQL expertise, and hands-on experience with Snowflake, Azure Data Factory, and Power BI. You should also be comfortable with dimensional modeling, medallion architecture, testing, observability, and fostering AI-ready data pipelines. Beyond technical skills, we value business-anchored thinking, curiosity about source ERP systems, strong teamwork and communication, and an ownership mindset that drives proactive debugging, automation, and continuous improvement. • Bachelor’s degree in Computer Science, Data/Information Systems, Engineering, Mathematics, or equivalent practical experience. • 2–3 years of professional data engineering experience. • 2+ years of hands‑on SQL development for analytical/ELT workloads. • 1–2 years of experience with Snowflake, Azure Data Factory, and Power BI semantic models. • Expertise in CTEs, window functions, analytic aggregates, and query performance tuning in Snowflake. • Knowledge of dimensional modeling, conformed dimensions, and SCD Type 1/2. • Familiarity with medallion architecture and data mesh concepts. • Experience with ADF orchestration, Key Vault integration, and robust error handling. • Ability to write unit and integration tests and participate in Git‑based CI/CD processes. • Understanding of security, RBAC, masking, and SOX/privacy implications for finance data. • Ability to frame engineering tasks around finance outcomes such as working capital and DSO. • Curiosity for source ERP/subledger systems and translating them into clean, governed assets. • Strong collaboration and communication skills with product owners, SMEs, architects, and security teams. • Proactive debugging, automation, and continuous improvement mindset. • Receptiveness to code reviews and adherence to data engineering standards.

Requirements

• ▸bachelor's
• ▸sql
• ▸snowflake
• ▸adf
• ▸powerbi
• ▸dimensional modeling

Work Environment

Office Full-Time