Description

• Drive performance tuning and optimization across Snowflake and Databricks environments to ensure efficiency at scale
• Enforce data quality, governance, and security compliance while managing large datasets, including SOX-relevant audit trails and lineage tracking
• Collaborate with Finance, Treasury, and Accounting stakeholders to translate business reconciliation requirements into scalable data solutions
• Work cross-functionally with data science and analytics teams to support ML/AI pipelines and feature engineering built on top of treasury and financial data
• Stay current on emerging data technologies and recommend enhancements to existing architectures.
• Skills:
• 5+ years of data engineering experience building and maintaining production pipelines
• Strong expertise in Databricks, Apache Spark (PySpark/SQL).
• Proven experience designing and managing data warehouses using Snowflake or equivalent cloud warehouse technologies
• Deep understanding of data modeling, SQL, and performance optimization
• Hands-on experience with AWS services —including S3, Glue, Lambda, and Redshift —for cloud-based data integration and pipeline orchestration
• Experience implementing ETL/ELT processes using cloud-native orchestration tools (e.G., Airflow, dbt, or equivalent)
• Solid knowledge of real-time or near-real-time streaming technologies (Kafka, Spark Streaming, or similar)
• Familiarity with ML/AI data pipelines and feature engineering best practices —experience preparing and serving financial data for downstream models
• Strong understanding of data quality, validation, and reconciliation patterns
• Strong communication and collaboration skills with the ability to work directly with business stakeholders in a fast-paced enterprise environment
• Ability to work independently and deliver with minimal direction

Key Skills

Education

Any Gradute