eNGINE builds Technical Teams. We are a Solutions and Placement firm shaped by decades of interaction with Technical professionals. Our inspiration is continuous learning and engagement with the markets we serve, the talent we represent, and the teams we build. Our Consulting Workforce is encouraged to enjoy career fulfillment in the form of challenging projects, schedule flexibility, and paid training/certifications. Successful outcomes start and finish with eNGINE .
eNGI NE is seeking a Data Architect to lead the design and delivery of our core data integration pipeline. This person will be responsible for consolidating data from multiple source systems across the organization into Snowflake, creating a unified foundation that the AI team can use to reason across financial data consistently — including recognizing and combining synonymous financial terms and metrics that may be labeled differently across systems.
What You'll Do:
- Design and build pipelines that pull data from a variety of source systems — including mainframe, SQL Server, Oracle, and cloud-based platforms — into a centralized Snowflake environment.
- Partner with multiple business and technical teams to identify, extract, and integrate relevant financial data sets.
- Develop approaches for identifying synonymous or overlapping financial terms/metrics across different systems and teams, and standardizing or mapping them so they can be reliably combined and analyzed together.
- Establish data architecture patterns, standards, and best practices for ongoing ingestion and integration work.
- Ensure data quality, consistency, and lineage are maintained as data moves from source systems into Snowflake.
- Collaborate closely with the AI team to ensure the resulting data foundation supports downstream AI/ML use cases.
What You'll Need:
- Professional experience within the finance/financial services industry.
- Demonstrated experience having led (or played a lead role in) a project that pulled data from multiple, disparate systems into a Snowflake environment — this must have been done at a financial company.
- Hands-on experience working with a range of source systems such as mainframe, SQL Server, Oracle, and cloud data platforms.
- Strong understanding of data modeling and the ability to reconcile similar/synonymous financial terms and metrics across different teams and systems.
- Solid grasp of data architecture and pipeline design principles, with the judgment to make sound structural decisions independently.
- Strong communication skills, with the ability to work cross-functionally with multiple teams and stakeholders.
Nice to Have:
- Prior exposure to AI/ML teams or supporting AI use cases with data infrastructure.
- Experience establishing data governance or metadata management practices.