Data Engineer

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. Responsibilities: Design, build, and optimize scalable ELT/ETL pipelines ingesting banking and treasury data from Kyriba into Snowflake and Databricks.

We are currently seeking a "Data Engineer" for a Contract role with one of our clients in New York, NY. Please apply if you are interested and available for it.

Title: Data Engineer
Duration: 06+ Months Contract
Location: New York, NY

Responsibilities:

• 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.

 
Qualifications:

• 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.

Regards,
Ishika Sharma
Cell: 973.967.3386
Email: ishika.sharma@artech.com