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Data Engineer
Posted: 08/21/2026
Job Number: 29133760
Job Description
Duration: 12 months contract with high possibility of extension
Location: New York City, NY 10003 (Onsite)
Duties:
Design, build, and optimize scalable ELT/ETL pipelines ingesting banking and treasury data from Kyriba into Snowflake and Databricks. Develop and own canonical data models and schemas for cash positions, bank transactions, intercompany settlements, and reconciliation outputs Architect data warehousing solutions ensuring seamless integration across cloud platforms and structured/unstructured data sources Collaborate with business stakeholders to understand data needs and develop high-performance solutions. Build and maintain reconciliation logic that compares Kyriba source data against GL systems (NetSuite) and surfaces discrepancies for Finance Operations Ensure pipelines operate with high availability, fault tolerance, and observability —including alerting, monitoring, and automated recovery
- 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.
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